Introduction to Brain-Computer Interface

#### **Chapter 1**

## Language as the Working Model of Human Mind

*Amitabh Dube, Umesh Kumar, Kapil Gupta, Jitendra Gupta, Bhoopendra Patel, Sanjay Kumar Singhal, Kavita Yadav, Lubaina Jetaji and Shubha Dube*

#### **Abstract**

*The Human Mind*, functional aspect of *Human Brain*, has been envisaged to be working on the tenets of *Chaos*, *a seeming order within a disorder*, the premise of *Universe*. The armamentarium of *Human Mind* makes use of distributed neuronal networks sub-serving *Sensorial Mechanisms, Mirror Neurone System (MNS)* and *Motor Mechanisms* etching a stochastic trajectory on the virtual phase-space of *Human Mind*, obeying the ethos of *Chaos*. The informational *sensorial mechanisms* recruit *attentional mechanisms* channelising through the window of chaotic neural dynamics onto *MNS* that providing *algorithmic image information flow along virtual phase- space coordinates* concluding onto *motor mechanisms* that generates and mirrors a *stimulus- specific and stimulus-adequate response*. The singularity of self-iterating fractal architectonics of *Event-Related Synchrony (ERS)*, a *Power Spectral Density (PSD)* precept of *electroencephalographic (EEG) time-series* denotes preferential and categorical *inhibition gateway* and an *Event-Related Desynchrony (ERD)* represents event related and locked gateway to stimulatory/excitatory neuronal architectonics leading to stimulus-locked and adequate neural response. The contextual inference in relation to *stochastic phase-space trajectory of self- iterating fractal of Off-Center* α *ERS (Central)-On-Surround* α *ERD-On Surround* θ *ERS* document efficient *neural dynamics of working memory.*, across patterned modulation and flow of the neurally coded information.

**Keywords:** Human Mind, Chaos, Stochastic Trajectory, Mirror Neurone System, Neural Dynamics, Electroencephalograph (EEG), Event Related Synchronisation (ERS)/Desynchronisation (ERD)

#### **1. Introduction**

#### **1.1 The multi-dimensional hierarchy of organisational levels in brain**

**Brains** are characterised by every property that engineers and computer scientists detest and avoid. They are **chaotic, unstable, nonlinear, non-stationary, non-Gaussian, asynchronous, noisy, and unpredictable in fine grain**, yet undeniably they are among the most successful devices that a billion years of evolution has produced. **Brain systems** operate on many levels of organisation, *microscopic, mesoscopic and macroscopic*, each with its scales of time and space. **Dynamics**, the modelling of change, is applicable to every level, from the atomic to the molecular,

**Figure 1.**

*Hilbert transform of EEG lead pair across EEG lead pair for EEG frequency-waveform bands of δ, θ, α, β, γ representing the* stochastic trajectory of neural dynamics in real-time.

and from macromolecular organelles to the neurones into which they are incorporated. In turn the **neurones** form populations, these form the sub-assemblies of brains, and so on up to embodied brains interacting purposively with the material, interpersonal, and politico-social environments.

Subsequently, the *mesoscopic* **level**, very aptly characterised by *nonlinear dynamical electroencephalographic (EEG) electrical activity* [1], seems to be the optimally suited substratum of interplay of neuronal discharge and its patterning, that seems to have been very beautifully and intelligently decrypted and decoded through the armamentarium of **digital biological signal processing across** *linear (relative and absolute power spectral densities, coherence and others)* **and** *non-linear classifiers (entropy, fractal dimensions and others)***.**

The varied discrete and quantal features of *Human Brain* working and co-opting, in tandem and in sync, across the dimensions and coordinates of space and time evolve into the *phase-space stochastic trajectory of* abstruse and arcane domain of *the Human Mind* observing the principles of *non-linear dynamics of Chaos [2]*. The human brain provides the scaffold and framework for the functional dynamics of human mind in real time [3] following the principles of *Chaos*, further documented by our centre in 2009 [4] (**Figure 1**).

*Carl Jung* has very aptly outlined the schema *as "In All Chaos There is A Cosmos, In All Disorder A Secret Order"*. The *Secret Order* as has been exemplified by Carl Jung forms the nidus to explore further the realms of *Chaos*.

#### **2. The working of the human brain**

The *Human Brain* communicates and interfaces through electrical and chemical processes in a *fractal* and *self-iterating fashion*. The neurones fire at a rate of 5–50/ second through *integrate-and-fire neurones and resonate and fire neurones* with a summated thought-processing time of around 329 milliseconds [5].

#### **3. Neurotransmitters**

The chemicals deployed by the *Human Brain* involve *neurotransmitters, neurohormones, neuropeptides, neuromodulators inclusive of dopamine, serotonin, acetylcholine, gamma aminobutyric acid (GABA), glutamate, glycine, adenosine triphosphate (ATP)* to name some of the chemicals. The neurotransmitters seem to be the key to functioning and influencing the neurophysiology of the *Human Brain* and are diffusely distributed with selective cerebral predominances responsible for the genesis of a select personality-trait brain waves and rhythms. The precursors to the *neurotransmitters, amino acids*, are readily available in the diet and the *diet* (and its interaction with the specific metabolic patterning of an individual) *determines the persona/qualia* of an individual.

#### **4. The rigid versus distributed functional patterning**


However, the modular aspect of the *Human Brain* with rigid configurations (as proposed by *Cajal* way back in 1913) has given way to the model of distributed neuronal networks that has resilience and the capacity to adjust and be flexible to the demands of internal and external milieu, wherein the mind-set with positivity influences and modulates the distributed neuronal pools and networks evolving the cognitive abilities of an individual.

A subtle and perceptible paradigm shift has been witnessed across the frontiers of *Neurosciences and Neurology* wherein **the** *Human Mind***, once thought to be working along the framework of modular architectonics**, is now envisaged to be traversing the alleyway along the distributed neuronal pools conjuring onto **dedicated and apportioned networks** that have the ability and the interface to **crosstalk**.

The building block scaffold of the respective dedicated neuronal pools is the archetypal *neurone* that has the endowed potential to respond in a *space and time coordinate-locked precept of action potential, the espoused all-or-none phenomenon* that incidentally happens to be the singular canonical principle of functional neurones. The armamentarium of neuronal language evolved through the presence and/or absence of *action potential all-or-none phenomena* along with **differential**

#### *Brain-Computer Interface*

#### **neuronal architectonics processing inclusive of serial, parallel, divergent, convergent, reverberating along with inter-neuronal reverberations** [7].

The unitary and singular neuronal tenet got segregated through the remarkable neurophysiological characteristic of *learning* **into dedicated neuronal pools** that became functionally conspicuous and perceptible as *sensory, mirror, motor and interneurones*. Such dedicated neuronal pools then evolved the distinctive patterned waveforms as evinced through electroencephalographic (EEG) signals [8] of *theta (***θ***), delta (***δ***), alpha (***α***), beta (***β***) and gamma (***α***) waves* and **such distributed neuronal pools** then evolved discrete neurodynamical phenomena of


#### **5. The human mind**

The *Human Mind* is the neurophysiological precept that tends to amalgamate the *Triune Brain Complex* through the distributed electro-chemical neural circuitry that follow the non-linear chaotic neural dynamics simulating the principles of Chaos in Nature [9]. *The primacy and singularity of chaos and chaotic systems (Complex Dynamic Systems) depict behaviours of determinism, paradox, self – generation, self – iteration, self – organisation, intrinsic unpredictability within the confines of the defined geometry across space – time that is sustained by the complex feedback loops. The qualia of chaotic systems include the sensitivity to initial conditions with disproportionate responsiveness to stimuli, the translatability from micro-through mesoscopic and macroscopic proportions, and the attractor-centring that is shuffled across space – time and is apparently a – causal (enfolded; implicate/ explicate), global singularity and is flexible and amenable to creation. The Strange Attractor-Centred Stochastic Trajectory so evolved through the neuronal oscillations* [4, 10] that sublimes the awe and grandeur of *Human Mind* seems to be the gateway and/or portal to flow of information that is legible, reproducible and stands the vagaries and vicissitudes of the flow of space and time.

In this backdrop and the chance brush and close encounter with *Chaotic Nonlinear Neural Dynamics of Human Mind* [4], our centre came across the novel finding of *Dysfunctional Mirror Neurone System ['Broken Mirrors' of Professor V. S. Ramachandran and Oberman [11]]* in children with *Attention Deficit Hyperactivity Disorder (ADHD)*, a disorder of social intelligence, an antecedent sequel to '*Broken Mirrors*', that was neurodynamically represented as the phenomenon of *Event-Related Synchrony (ERS) of mu rhythm (alpha waveform along somatosensory EEG lead pairs)* [12]) when the ADHD participant children aped and imitated the action protocol of hyperventilation, while an *Event-Related Desynchrony (ERD)* was observed in the similar rhythm of *mu waveform* in EEG lead pairs of normal control children.

The *Human Mind* replicates the transmutation and metamorphosis of the nonlinear dynamics of chaos wherein a fine interplay between matter and energy takes place, i.e., the abstruse versus the intangible with quantum shift being appreciated through the perturbations of space–time synthesising *sensory–mirror–motor neurones–cognition* tangible precepts plunging along the ethos and tenor of chaos, journeying to the most fundamental or primal state of consciousness – Chaos, when

#### *Language as the Working Model of Human Mind DOI: http://dx.doi.org/10.5772/intechopen.98536*

shift in primal image of self becomes possible through its de-structured nature in entirety. In this qualified state of Chaos, the Human Mind evolves onto a rhythm/ pattern that seems to be reverberating with Cosmic Consciousness.

It is conceivable that the sensorial stimulus evinces a characteristic *event/stimulus-related synchrony (ERS)* **of** *theta (***θ***) wave-form* reflective of an antecedent and incidental entrainment of *attentional neuronal mechanistic resources* that seemingly feeds onto and opens the portal of the algorithmic flow of *mirror neurone system arsenal through means of event/stimulus-related desynchrony (ERD) of alpha (*α*) wave-form that seems to feed onto the motor neuronal system responding through ERD to effect a cogent, logical and stimulus/event-locked response.* Such a model of intricate dance of *event/stimulus-related synchrony (ERS)* **of** *theta (***θ***) waveform and event/stimulus-related desynchrony (ERD) of alpha (***α***) wave-form* [13, 14] has been hypothesised to be the mainstay of the working *Human Mind*.

The *Human Mind* is conceived as an entity forming the functional singularity of *Human Brain* that evolves through the integration of *quantum mechanics of waveparticle espousing the inter-convertibility of mass into energy waveform and vice versa, the Higgs Boson being the interface and the amalgamating particle.*

A set of neuronal pools, *referred to as fractals with the inherent capability of selforganising and self-iterating,* are recruited to sub-serve a distinct selected function limited by the coordinates of space–time with a time decay of 2–3 seconds recouped and retrieved by another set of neuronal pools observing similar fractal neurodynamical dimensions of synced *ERD* and *ERS*. The set of neuronal pools that evolve during the course of time rhyme and oscillate with a specific wave-pattern that is construed and translated onto the *stochastic phase-space trajectory with the strange attractor* specific for the function being attended to silhouetting and profiling the *Human Mind*.

Taking the analogy further, *Cosmic Consciousness* seem to be the predicate of *mass-energy wave-form interface* as exemplified by the *God particle, Higgs Boson*. The effervescent and evolving Human Mind works on the same principle of Cosmos with a tendency to cohere and sync with the flow of Cosmic Consciousness.

#### **6. The working model of language**

The working of *Human Mind along with its functional and morphological correlates* has been an arena that has overwhelmed and beguiled mankind since times immemorial*.*

The Neurophysiologists and Cognitive Neuroscientists have resorted varied procedures, both non – invasive and invasive, to gain an insight and to reveal the mystics of *working human mind*, wherein *Electroencephalography (EEG)* and *Event Related Potentials (ERPs)* [15–17] provide the desired armamentarium to record underlying neural dynamics of human mind in real – time, through precepts of flow of space and time namely, amplitude and latency, respectively, that are time-locked to specific sensory, motor and/or cognitive modalities of stimuli [18].

**EEG and ERPs seem to be the tools with temporal precision but poor spatial localisation** for appreciation of underlying neuronal dedicated networks and their dynamics for various higher mental and cognitive functions to identify, isolate and register across space – time, the physical qualia of the stimulus (features detection, the so-called feature-detectors). The neural dynamics of working memory have been envisaged to be funnelled onto the language acquisition processes and the interplay between multiple frequency wave-forms in the cortical neural networks play an elementary deciding role in such an intricately woven process [19–22].

Neurolinguistics, an interdisciplinary domain that draws in inputs from application disciplines of neurosciences, linguistics, cognitive sciences, computers electronics and communications, neuropsychology and neurophysiology, and basic sciences of mathematics and physics, explores the underlying neural mechanisms of human brain and its correlation with the *phenomenon of the means of communication, that is* **Language**.

#### **7. The ontogeny of language: The piggyback ride of working memory**

At birth young infants exhibit a universal capacity to detect differences between phonetic contrasts used in world's language [23]. The mother (or father) has to entrain the attentional mechanisms of the child through *Social Gaze* with subsequent motherese (or fatherse or parentese), a form of language that involves lot of changes in pitch, is melodious and repetitive. *Social Gaze or Eye Contact* with the mother forms the essence or pre-requisite of genesis of language, wherein the *vowels* (and that too the *extremes of vowels, i.e., 'a' and 'o' 'u'*) precede *consonants* for the mere fact that lips movements is maximal for vowels and due to the simplified mechanism(s) that underlie the *neurolinguistics* of vowel. The language development or transition of the human mind onto the axes of language has been hypothesised to take place along two neural phases, namely *Phase I* and *Phase II.*

#### **7.1 Phase I (neurodynamical phase)**

*The neurodynamical phase* also known as the *general open-system* is uncommitted and open to change and plasticity and is the phase where priming of the human mind takes place. The universal capacity of the human mind is dramatically altered by the language experience starting as early as 6 months for *vowels (a, e, i, o, u)* and by 10 months for *consonants*. The *extremes of vowels, namely 'a', 'o' and 'u'* involve maximal movements of the lips that the child gets enamoured through the mental landscape so formed by the stochastic trajectory initiated in the *Phase-Space of Human Mind* by the system of *Mirror Neurones.*

#### **7.2 Phase II (linguistic phase)**

It represents the language specific phase wherein the human mind becomes committed to the specific language that is being acquired and usually starts from end of the first year of life. Neural oscillations across the coordinates of time (brainwaves), within individual neurones or through interactions among neurones, *are rhythmic or repetitive patterns of neural activity of the central nervous system* and *such patterned neural dynamics signify and describes the respective neurophysiological functional characteristics*. The techniques of *Biological Signal Processing (BSP)* have been employed to classify and categorise EEG signals through linear domain of power spectral density (PSD), linear discriminant analysis (LDA) and varied non – linear domains of neural networks.

The concept of human mind in acquisition of language or general learning mechanism(s) contribute to such an evolved mechanism of spoken and written language that imprisons the mechanistic of mirror neurone system (MNS) and *synaptic neuroplasticity*. *MNS* plays a pivotal role and is considered to be an interface between the qualia of sensorium and motor system of the intricately woven *Human Mind*, wherein activation of *Mirror Neurone System* initiates the process of image formation in the virtual phase-space trajectory of human mind so evolved by the

*Language as the Working Model of Human Mind DOI: http://dx.doi.org/10.5772/intechopen.98536*

baseline reverberating chaotic neural dynamics, a phenomenon learnt and hard-wired through the neurophysiological process of memory.

**The neural signature of** *Working Memory (WM), the primacy of emergent Human Mind* [24]*,* for *Encoding, Registration and Retrieval of Memory* [25, 26] inputs has been postulated to be served by *three EEG Wave-Forms Complex of Theta* [27, 28]*, Alpha and Gamma frequency bands* [10] with a bootstrapping blueprint wherein the *gamma wave-forms or bursts hitchhike or piggy back rides the theta wave responsible for feature detection* **along with** *alpha-theta wave-form that coincidentally allocates attentional resources onto the evolved dedicated neuronal circuitry that are stimulus-specific* [29, 30]. These frequency oscillations have been observed to **modulate neuronal excitability** by controlling neuronal firing, and could be responsible for **holding of stimulus-specific information in space and time along the coordinates of working memory neuronal pool** [31]. Such a neural synchronisation proposal may provide a solution to underlying mechanism(s) of synthesis and amalgamation of features of an object through coordinated firing patterns that in essence underlie the *feature detector mechanism(s) of neuronal process* [32].

It has been envisioned that,


Pfurtscheller and Klimesch [38]*,* Pfurtscheller and Aranibar [39] and Pfurtscheller and Lopes da Silva [40] had reported that during visual stimulation **alpha wave-form desynchronises** giving rise to *ERD* **over occipital recording sites** whereas **over motor cortex synchrony in form of** *ERS* could be observed. Sauseng et al. [41], [42] put forward the observation of **change in** *PSD of alpha wave-form* **that is observed at the occipital and pre-frontal areas during** *topdown processing in a working memory task*, wherein a *decrease in alpha PSD power at occipital site* with a **consequential** *increased alpha PSD power is observed at prefrontal EEG electrode site*. **The** *ERD quantum* **of alpha frequency wave-form during encoding** in a visual working memory task has been correlated with the memory load [29, 30, 33–36].

**However**, [43] reported that the processing of working memory of **encoding and retention involves the oscillatory activities along multiple frequency bands of EEG wave forms inclusive of** *alpha frequency as well* through local and longrange neural networks proposing the existence of **multiple parallel functional mechanisms of alpha oscillations** [44]. In this context of equivocal representation of alpha oscillations, it would be interesting to examine changes in alpha oscillations pattern that could be sensitive and characteristic to working memory task.

The observation documented from our laboratory of *theta wave form band synchrony***, known as** *Event-Related Synchrony (ERS)* **mirroring increased** *PSD,* across distributed range of task relevant areas of brain namely,


**registration, retention and retrieval** processing is reflective of dynamical linking, an observation that had been documented by EEG studies of [41, 42, 45] as well, though [46–48] could not appreciate such breakthrough linkage (**Figure 2**).

#### **Figure 2.**

*Power spectral densities (PSDs) of theta frequency wave form in three memory conditions of retention, semantic manipulation and backward manipulation with raw EEG data being processed through BESS software where epochs (epoch length = 1000 ms) were separated for each trial [54 trials being part of delayed-match-to-sample (DMTS) task] and data was averaged separately respectively for each electrode for each condition (FP1, FP2, F7, F3, AFz, Fz, F4, F8,T3, C3, Cz, C4,T4, P7, P3, Pz, P4, P8, O1, O2 electrodes were selected). ERS as evinced through enhanced PSD (increase in mean amplitude power in sq. microvolts), was observed in theta wave-form in all three conditions/manoeuvres of retention (FZ, F3, F4, F7,T3,T4) semantic forward information processing (FP1, FP2, AFZ, FZ, F3, F4, F7, F8, C3, P7,T3,T4) and backward information processing (FP1, FP2, AFZ, FZ, F3, F4, F7, F8, C3,T3,T4) of EEG electrode pairs and on comparative evaluation with basal EEG time-series run along said EEG electrode pairs, significant difference in PSD could be appreciated only along T4 EEG electrode pair in conditions of retention (p = 0.05), semantic manipulation (p = 0.05) and backward manipulation (p = 0.01) by using one way ANOVA at 5% level of significance.*

The assessment of power-spectral density of EEG signals from our laboratory paved the way for appreciation of closely intertwined intricate dance of *ERS/ERD along the coordinates of space and time that probably seems to be the flip-flop switch for the flow of corporeal and legible information* (**Figure 3**) [49]. The ERS of theta waveform with significantly appreciable change in Power Spectral Density (PSD) at EEG electrode pair of T4 (**Figure 1**) along with concomitant ERD of alpha wave form skewing onto left hemisphere lateralisation of neurophysiological processes [as exemplified by Oblique Lateral Asymmetry Index (LAI)] during the select conditions of retention, semantic manipulation and backward manipulation (**Figure 2**) is yet another example of concomitant stimulatory and inhibitory dedicated neuronal pools that evolve during and are responsible for the stimulus-specific adequate response. More likely, the looped fractals of neuronal pools (modules) of on-centre ERS theta wave form, on-centre ERD alpha and offsurround ERS alpha or off-centre ERS alpha and onsurround ERD alpha have a tendency to self-iterate that tends to etch the stochastic trajectory along the Human Mind Phase-Space.

**The characteristic of temporal distribution of** *ERS/ERD PSD* **along the run of EEG time-series** was evaluated in our laboratory and during the **DMTS task**, **the temporal distribution across** *two frequency bands of theta and alpha* was accessed to assess neuronal oscillatory activities during **WM tasks** across select cortical regions [50–52] and to assess modular memory facets and processes that entrain dedicated self-iterating fractals of neuronal pools in human brain resulting in memory consolidation processes concluding into **language acquisition, manipulation and comprehension processes.**

*Language as the Working Model of Human Mind DOI: http://dx.doi.org/10.5772/intechopen.98536*

#### **Figure 3.**

*Portrays Lateral Asymmetry Index (LAI) ratio of different conditions of Alpha Frequency Band at P3-P4 EEG electrode site. Significant difference could be appreciated at parietal region of P3-P4 EEG electrode pair along alpha frequency band using T-test with p = 0.004, p=0.002 (p < 0.05) at 5% level of significance with left hemisphere lateralisation (skewed neurophysiological processes) during retention condition and semantic manipulation condition, respectively. In backward manipulation condition, significant difference could be appreciated at additional parietal EEG lead pairs of P7-P8 besides P3-P4 with p = 0.02 in both electrode pairs (p < 0.05) at 5% level of significance with left hemisphere lateralisation. 1 = Control Condition, 2 = RetentionCondition, 3 = Semantic Condition, 4 = Backward Condition. LAI = [P (left) - P (right)]/[P (left) + p (right)].*

**These chunks of information or memory codes** might generate a **particular patterned rhythm** which later during **retrieval** of information from dedicated neural networks might follow the phenomena of **pattern matching** during its response for same memory inputs.

**Figures 4** and **5** depicts *ERD/ERS percentage change {ERD% = (Actual Power-Reference Power)/Reference Power 100} of Power Spectral Densities of Alpha frequency wave form* when compared among male and females in *Retention Condition* where significant difference could be seen at CZ, P8, T4 electrode sites and **Figure 5** displays results of *ERD/ERS alpha activity i*n *Semantic Condition*, exhibiting significant differences along CZ, P4, T4 EEG electrode sites. The common denominator appreciates the intricate interwoven *Off-Centre* **α** *ERS (Central)-On-Surround* **α** *ERD Neural Dynamics* as could be deduced and envisioned from observations of **Figures 4** and **5** that seem to be intertwined and interlocked through observations of findings of **Figures 2** and **3**, with the self-iterating trajectorial pathways of the

#### **Figure 4.**

*Depicts ERD/ERS percentage change [ERD% = (Actual Power-Reference Power)/Reference Power 100] of Power Spectral Densities of Alpha frequency wave form when compared among males and females in Retention Condition where significant difference could be observed at CZ, P8 and T4 EEG electrode sites.*

#### **Figure 5.**

*Illustrates ERD/ERS percentage change {ERD% = (Actual Power-Reference Power)/Reference Power 100} of Power Spectral Densities of Alpha frequency wave form when compared among male and females in Retention Condition where significant difference could be seen along CZ, P4,T4 EEG electrode sites.*

looped alpha and theta wave forms through respective precepts of Event-Related Synchrony (ERS) and Event-Related Desynchrony (ERD).

In this context and with the characteristically patterned observations data from the present study [*Neuropsychological Trends* in print] the precepts of *Neural Dynamics of Working Memory Model* has been conceptualised as:


#### **Figure 6.**

*ERD/ERS percentage change of Power Spectral Densities (PSDs)of Theta frequency wave form when compared among male and females in all the three conditions where significant difference could be appreciated at F3 in Retention condition; at FP1,F8 in Semantic manipulation condition and at AFz in Backward manipulation condition and results could reflect same as Alpha frequency wave form that females outperformed in the visuospatial DMTS task compared to males using T-test at 5% confidence level. The contextual inference from the present study in relation to the above stochastic phase-space trajectory of Off-Centre α ERS (Central)-On-Surround α ERD-On Surround θ ERS document a significantly enhanced PSD values of said trajectorial path in females as compared to that observed in males, endowing the female gender with neurophysiologically efficient neural dynamics of working memory.*

*Language as the Working Model of Human Mind DOI: http://dx.doi.org/10.5772/intechopen.98536*

• **The** *Backward Processing in Memory warrants a similar Theta ERS-LAI Alpha ERD looping along temporal and parietal region (with additional inputs from parietal areas).*

The hypothesis posited is that the concept of *Neural Dynamics of Working Memory Model* reflects as under:


It seems that there are two aspects of processing of LTM in terms of mean PSD and LAI along theta and alpha frequency waveforms.


The precept of *Hemispheric Encoding/Retrieval Asymmetry (HERA)* so documented had been first hypothesised by Tulving et al. [53] supported by Nyberg et al. [54] as well that advocates the premise of **preferential and skewed involvement of left hemisphere in semantic (algorithmic non-linear neural information flow) retrieval and encoding** whereas **right hemisphere seems to be more involved with the episodic retrieval.**

The visual sensory inputs/information so perceived in the form of varied protocols of *Delayed Matched to Sample Task (DMTS)* is essentially relayed to primary visual cortex underlying EEG occipital region electrode pairs where information is processed. Primary visual cortex (V17) [55] subserves the qualia of perception and visual association areas (V18, 19) [56]. Lisman and Jensen [57] concluded **the process of recognition through** *patterned-matching of the gamma-burst, alpha-theta waveforms looping or the bootstrapping (piggy-back riding) of gamma burst onto alpha-theta combine waveforms***.** The visual inputs as a part of visuo-spatial *DMTS* are perceived by occipital region and it has been modelled [58] that such visual impulses are then translated and transmogrified into auditory impulses in the differently-abled angular gyrus (anterolateral region of parietal lobe, near the superior edge of temporal lobe and immediately posterior to the supramarginal gyrus), a feature that could be observed as *increase in the*

*amplitude (ERS) of Theta Frequency wave-form in EEG*. The visual–auditory interface impulse is then transferred onto Wernicke's area/auditory neural codes (Brodmann area 22, superior temporal gyrus) in order to appreciate and decode the semantics of visuo-auditory interface impulse perceived as symbols, letters, words and matching sounds accordingly [59].

*ERS of Theta Waveform so evolved by interacting stimulus-locked dedicated neuronal pools with ERD of Alpha waveform functionally and neurophysiologically representing the dedicated reverberating mirror neuronal pool system seems to be representing the working model of Human Memory-Language.* It seems that the generation of language shapes into the virtual stochastic phase-space of human mind through the help of **reverberating Lateral Asymmetry of** *Alpha wave-form ERD, representative of Mirror Neurone System (MNS)*. Previous studies have reported that *Alpha ERD during motor response in a WM task has been interpreted as the preparation of a movement-specific motor task* but does not reflect processing for the specific task itself [43, 60]. The alpha ERD in the sensorimotor system may buttress the concept of **a preparatory role of alpha ERD**. Alpha ERD had been also posited even during anticipation of an event [61], again emphasising the role of preparation for a motor response. In this background, the *role of alpha ERD* could be perceived as developing a preparatory *schema intricately interwoven with the Mirror Neurone System (MNS) creating and evolving an image (an alter-image in the stochastic phase-space of Human Mind)* during the ensuing encoding interval.

The findings of *ERS in theta wave-form with a significant change in PSD along select EEG electrode pairs a recent study from our laboratory* have also been reported by [62, 63], though Burke et al. [21] and [46, 47] could not observe such a patterned and locked differential EEG theta wave-form PSD during the manoeuvres of retention, semantic and backward manipulation and hypothesised a possibility of contextual overlapping between encoding and retrieval tasks.

The above documentation of *ERS Theta Frequency waveform bootstrapping with concomitant ERD of Alpha Frequency waveform seem to evolve an envelope of Working Memory that translates into a comprehensible means of communication, Language.* The interplay between these frequency wave-form forms the ground of working memory which is thought to be an important constituent component instrumental in language acquisition, comprehension and manipulation. The amount of information/memory inputs restricted by day-to-day working memory might be useful and can be considered as the focus for processing and acquisition of language e.g., semantics of letters and words (positioning and placement), syntactics of words (reproducible neurodynamically grammatically cogent disposition/ sequence), word frequency, plausibility, discourse context, intonational information, etc.

The processing of letters or words in the form of memory inputs give an insight into the underlying neuro-physiological processing and neural dynamics responsible for the evolution and progression of the evolved phenomena of written and spoken language that make use of semantic and episodic memory. The *EEG Power Spectral Densities (PSDs) of alpha frequency band during semantic memory and information processing* and *the PSDs of theta frequency band during episodic memory and information processing* that follow separate paths in their nativity could be responsible for holding relevant information across coordinates of space and time (freezing the flow of space and time in the process) providing a gateway for synthesis of a structured and evolved system of communication, known as *language*.

The above observations create the platform for an integrating function and role of principles of *Working Memory in generation and evolution of a synthesised and coordinated communication system as outlined by the structured Language of Human Mind.*

#### **8. The arena of language acquisition: probable neural substrates and signature**

The major debate regarding *neural substrates underlying language acquisition (inclusive of the capacity to detect phonetic distinction and develop language – specific phonetic capacity and acquire legible, valid and comprehensible words)* lies in the belief if nativist (innate rather than acquired) domain – specific dedicated neural mechanism(s) operate exclusively on linguistic data, wherein the neural architecture is decided beforehand for an individual in acquisition of language or general learning mechanism(s) contribute to such an evolved mechanism of spoken and written language. The nativist approach posits the universal capacity to detect differences in phonetic contrasts in all languages. It has further been hypothesised from ERP studies that the response profile of *Human Mind* in terms of ERPs that are locked in space and time to varied phonetics is a significantly important component contributing to elementary building blocks of language and initial language phonetic learning is an essential pathway to learning.

Hence, it seems that **the fine dance of** *ERS Theta Frequency wave-form* **observed at temporal EEG lead pair** *closely looped with LAI of Alpha Frequency wave-form ERD* **seem to evolve a synthesising envelope of** *Working Memory* that translates into comprehensible means of communication, *Language*. The *theta and alpha frequency waveforms* with the available resources, the interplay between these frequency waveforms, initiate the ground of working memory which seemingly is hitched-hiked onto **language acquisition, comprehension and manipulation**. The dynamical power spectral interplay of *theta and alpha frequency waveforms* along the coordinates of space and time during the *Working Memory* tasks of *retention, semantic (forward processing) and backward processing* seem to form the *gateway of primacy* opening the portal of algorithmic flow of neural information so needed for the neurocognitive primacy of language. The amount of information/ memory inputs constraint by quotidian working memory might be utilitarian and can be considered as cynosure for processing and acquisition of language e.g., semantics of letters and words, syntactics of words, word frequency, plausibility, discourse context, intonational information, to name some of the intricate and fascinating nuances.

Subsequently, it is conceived that self-iterating fractal of interacting *ERD* and *ERS* through respective frequency waveforms *theta (*θ*)* and *alpha (***α***) waveforms* is construed with θ*waveform band singularity of ERS* across frontal and midline regions with antecedent **α***ERD* across respective mirror neurone system domain along with**α***ERS* at central region. The singularity of *ERS* denotes a preferential and categorical *inhibition gateway* and an *ERD* represents an event related and locked gateway to stimulatory/excitatory neuronal architectonics presumably responsible for *stimulus-locked and adequate neural response*. The fine and intricate interplay of θ *ERS (frontal and midline areas fine-tuned excitation)*, **α** *ERD (parietal and temporal floral activation) and* **α** *ERS (central selective inhibition)* evolves the self-evolving florid landscape of an **ERD on-centre and ERS off-surround loci along with an ERS off-centre and ERD on-surround**. The evolution of *frontal and midline excitatory* θ *ERS* along stochastic phase-space trajectory is a reflection of an evolving *fractal self-iterating excitatory gateway with antecedent fine-tuned channelisation of attentional mechanisms onto the stimulus/event restricting extraneous interfering neural mechanisms in the process*. The florid **α** *ERD* is representative of an evolving *excitatory stochastic phase-space trajectory dynamically mirroring the functional Mirror Neurone System (MNS)* responsible for algorithmic information flow onto subsequent *MNS* along with antecedent *central selective inhibition through* **α** *ERS inhibiting interfering contrivances (an example of* **α** *ERS Off-Centre with* **α** *ERD on-surround with*

**Figure 7.** *The theta (θ) ERD on centre, alpha (α) ERD on centre and alpha (α) ERS off surround model.*

*Language as the Working Model of Human Mind DOI: http://dx.doi.org/10.5772/intechopen.98536*

θ *ERS on-surround).* These self-iterating fractal architectonics of *central inhibitory, Off-Centre* **α** *ERS, surround excitatory on-surround* **α** *ERD and on surround* θ *ERS,* representative of i*nterwoven PSD singular Off-Centre* **α** *ERS (Central)-On-Surround* **α** *ERD-On Surround* θ *ERS phenomenology* seem to define the qualia and quanta of underlying neural mechanisms of *working memory* (**Figure 7**).

The contextual inference in relation to *stochastic phase-space trajectory of Off-Centre* **α** *ERS (Central)-On-Surround* **α** *ERD-On Surround* θ *ERS* document a neurophysiologically efficient *neural dynamics of working memory* (**Figure 8**) [49]*.*

The above model envisages a self-iterating fractal of *θ ERS On-Centre along with α ERD On-Centre and α ERS Off-Surround* mirrored along *θ ERS On-Centre, α ERS Off-Centre and α ERSD On-Surround, the so-called EEG micro states that tend to oscillate through the execution of the respective cognitive manoeuvre and these selfiterating fractals of lateral asymmetry index (LAI) of alpha (α) ERD and ERS along with theta (θ) ERS tend to open the gateway/portal of effective cognitive network.*

In this connectome, the *Human Mind* is envisaged as an esoteric concept that probably represents a logical synthesis of functional *mass* and *energy*, so represented by the characteristically patterned modulation and flow of the neurally coded information.

In conclusion, the neural architectonics subserving *language* seem to evolve across the self-iterating fractal features of phenomenology of *on-centre/off surround and off centre/off surround of ERD and ERS represented through electroencephalographic frequency waveforms of* θ*ERS,* α*ERD and* α*ERS* that synthesise and evolve the fine-tuned cognate neural mechanisms that evolve into the structured means of communication, *language.*

### **Contributors**

Amitabh Dube, M.D., Umesh Kumar, M.D., Bhoopendra Patel, M.D., Lubaina Jetaji, M.Sc., Kapil Gupta, M.D., Jitendra Gupta, M.D., Sanjay Kumar Singhal, M.D., Kavita Yadav, M.Sc., Ph.D., Shubha Dube, Ph.D

#### **Acknowledgements**

The authors are greatly indebted to **Late Professor (Dr.) Ashok Panagariya**, a *Neuroscientist of International acclaim*, whose piercing insight and vision had been and will remain *The Fountainhead* of the ongoing work on *Modelling of Human Mind*.

#### **Author details**

Amitabh Dube<sup>1</sup> \*, Umesh Kumar<sup>2</sup> , Kapil Gupta<sup>1</sup> , Jitendra Gupta<sup>1</sup> , Bhoopendra Patel3 , Sanjay Kumar Singhal<sup>1</sup> , Kavita Yadav<sup>1</sup> , Lubaina Jetaji<sup>1</sup> and Shubha Dube<sup>4</sup>

1 Physiology, S.M.S. Medical College and Attached Hospitals, Jaipur, Rajasthan, India

2 Physiology, Government Medical College, Kota, Rajasthan, India


\*Address all correspondence to: amitabhdube786@gmail.com

© 2021 The Author(s). Licensee IntechOpen. This chapter is distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/ by/3.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

*Language as the Working Model of Human Mind DOI: http://dx.doi.org/10.5772/intechopen.98536*

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#### **Chapter 2**

## A Brief Summary of EEG Artifact Handling

*İbrahim Kaya*

#### **Abstract**

There are various obstacles in the way of use of EEG. Among these, the major obstacles are the artifacts. While some artifacts are avoidable, due to the nature of the EEG techniques there are inevitable artifacts as well. Artifacts can be categorized as internal/physiological or external/non-physiological. The most common internal artifacts are ocular or muscular origins. Internal artifacts are difficult to detect and remove, because they contain signal information as well. For both resting state EEG and ERP studies, artifact handling needs to be carefully carried out in order to retain the maximal signal. Therefore, an effective management of these inevitable artifacts is critical for the EEG based researches. Many researchers from various fields studied this challenging phenomenon and came up with some solutions. However, the developed methods are not well known by the real practitioners of EEG as a tool because of their limited knowledge about these engineering approaches. They still use the traditional visual inspection of the EEG. This work aims to inform the researchers working in the field of EEG about the artifacts and artifact management options available in order to increase the awareness of the available tools such as EEG preprocessing pipelines.

**Keywords:** Artifact, Artifact removal methods, EEG, EEG preprocessing, Muscular artifacts, Ocular artifacts, Preprocessing pipelines

#### **1. Introduction**

A signal is a function that conveys information about the behavior or attributes of some phenomenon [1]. On the other hand, information can be anything. A waveform can have multiple overlapping information in the same space–time. The signal in a waveform is subjective, it can be color for one and shape for the other. In electrophysiology, waveform under inspection can be separated into two as the signal of interest and noise. The signal can be electrocardiography (ECG), Electroencephalogram (EEG), or any other physiological signal, noise is any unwanted wave source ınterfering with the signal. If we consider EEG as the signal, it is recorded from the scalp by electrodes and consists of the overall electrical activities of neural populations and a contribution of glial cells [2]. EEG has a wide range of use in both clinical practice and engineering applications in medicine, particularly neurology, sleep, and epilepsy research.

#### **2. Background**

The EEG recording environment and subject related electrical activities during recording deteriorate the signal quality. Artifacts are undesired signals that may

introduce changes in the measurements and affect the signal of interest [3]. EEG can be contaminated in frequency or time domain by artifacts that are resulted from internal sources of physiologic activities and movement of the subject and/ or external sources of environmental interferences, equipment, movement of electrodes and cables [4]. Artifact types and sources are listed in the **Table 1**. External artifacts can be prevented by proper shielding, grounding cables, isolating and moving cables away from recording sites since they act as antennas during operation. On the other hand, internal or physiological artifacts are challenging for researchers because of their inclusion of signal or resemblance to the signals. The most important artifacts in a typical EEG recording are ocular electro-oculogram (EOG) artifacts and muscular (EMG) artifacts.

#### **2.1 Ocular artifacts**

Electrical potentials due to eye opening/closure, blinks, eyelid flutter and eye movements propagate over the scalp and produce hostile EOG artifacts in the recorded EEG. Eye movements are major sources of contamination of EEG. The origin of this contamination is disputable. Cornea-retinal dipole movement, retinal dipole movement and eyelid movement are the three main proposed causes of the eye movement related voltage potential [6]. The direction of eye movements affects the shape of the EOG waveform while a square-like EOG wave is produced by vertical eye movements and blinks which leads to a spike-shaped waveform [7]. Blinks


#### **Table 1.**

*EEG artifact types and sources. Adapted from [4, 5].*

#### *A Brief Summary of EEG Artifact Handling DOI: http://dx.doi.org/10.5772/intechopen.99127*

which are attributable to the eyelid moving over the cornea, occurring at intervals of 1-10s, generate a characteristic brief potential of between 0.2 s and 0.4 s duration due to eyelid movement over cornea [8, 9]. The blinking artifact generally has an amplitude much larger than that of the background EEG [6]. It is advantageous to have a reference EOG channel during EEG recording for the cancellation of ocular artifact from EEG activity [3].

#### **2.2 Muscular artifacts**

Electrical activity on the body surface due to the contracting muscles are recorded via Electromyogram (EMG) [3]. Since independent myogenic activities of head, face and neck muscles are conducted through the entire scalp, it can be monitored in the EEG [10, 11]. The amplitude of this type of artifact is dependent on the type of muscle and the degree of tension [3, 12]. The frequency range of EMG activity is wide, being maximal at frequencies higher than 30 Hz [13, 14].

#### **2.3 Cardiac artifacts**

The electrical potential due to cardiac activity can exhibit itself in the EEG as ECG artifacts. Typical high frequency waveforms similar to EKG P-QRS-T shape are characteristics of EKG artifacts in EEG [15].

#### **2.4 Other artifacts**

Head, body and limb movements cause irregular high voltage artifacts. Artifacts can be produced by tremors in patients such as Parkinson disease and movement disorders. Changing patient position into a calm comfortable stable position helps reducing artifacts. Another prevention for respiratory related movement artifacts is to use a towel or a firm material support for the neck. The changes in the impedance or electrical potential between scalp and electrode may cause electrode artifacts. These can result from poor electrode contact, broken lead, electrolyte gel insufficiency. This type of artifact usually exhibits itself in sudden electrode pops. These electrode artifacts can be eliminated by using proper electrolyte gel, checking electrode impedance, changing the broken electrodes, and shifting the electrode position slightly.

#### **3. Artifact handling methods**

A typical EEG recording system is shown in **Figure 1**. At the heart of a recording setup is the biopotential amplifier. It should have high common mode rejection ratios, however it should not have high gains, this can saturate the signal due to large half-cell potentials at the electrodes. Unequal electrode impedances are major sources of common mode artifacts such as powerline.

Environmental artifacts can be eliminated by bringing the electrodes leads closer together, moving the electrodes and subject away from the noise sources, using single isolated earth for the whole setup, and shielding the cables, machines and artifact sources with a metal tape connected to the common earth. Moreover, the environmental conditions should satisfy the following requirements for proper recordings. These can be listed as, quiet atmosphere, comfortable temperature and humidity, controlled proper lighting, using a comfortable bed or chair, and separating the powerline of the EEG system from the other machines in the lab.

**Figure 1.**

*EEG recording system and experiment setup.*

#### **3.1 Averaging methods to suppress ERP artifacts**

Event Related Potentials (ERP) are electrical signals generated in response to internal or external events and they are recorded by EEG [16]. In evoked potentials, each stimulus produces an evoked potential embedded in EEG. However, since the ERP or evoked potential signals are generally subtle in EEG, averaging of many epochs are needed to make them distinguishable. An ensemble averaging method to enhance the ERPs was defined by [17]. This relies on the assumption that by synchronous averaging of each epoch, signal ERP amplitude adds constructively and EEG background noise diminishes destructively.

In ERP and evoked potential research, artifacts contaminate the final ensemble average signal of interest. One method to overcome this adverse effect is to benefit from a weighted averaging [18]. In weighted averaging technique each epoch is weighted inversely with the non-stationary noise maximum amplitude in the epoch. In [19], each trial's contribution to ensemble average is multiplied by a weight according to its correlation with the rest of the data. This factor is inversely related to its probability of being an artifact. For example, a large amplitude EEG is likely to be an artifact and the contribution factor for the trial involving large amplitudes will be low whereas the factor for a small amplitude EEG is high (**Figure 2**). Davila and Mobin [20] showed that weighted averaging of auditory EP has higher SNR than

**Figure 2.** *Various EEG artifacts are shown.*

#### *A Brief Summary of EEG Artifact Handling DOI: http://dx.doi.org/10.5772/intechopen.99127*

conventional ensemble averaging. John et al. [21] studied the effects of such techniques as sample-weighted averaging, noise-weighted averaging, amplitude based artifact rejection, percentage based artifact rejection, and normal averaging on the steady state auditory evoked potentials. It concluded in favor of weighted averaging for better SNR of steady state responses. On the other hand, according to [22], weighted averaging underestimates the ERP signal amplitude. Determination of the optimal weighting factor is not straightforward and this limits the performance of the weighting averaging method. Mühler and Specht [23] developed a method called 'sorted averaging'. In sorted averaging, epochs are sorted with RMS values from small to large, since noisy artifactual epochs have large RMS values compared to low noise signals. The signal averaging is performed by addition of epochs from the low noise RMS to large RMS sorted order until a maximum peak of SNR2 is obtained [24]. This eliminates the high RMS noisy epochs and yields a better ERP waveform. Compared to weighted averaging, sorted averaging had significantly higher SNR2 [23].

Median averaging is another approach to ERP artifact handling and it is based on taking the median points of all the epochs and adding them to form a median average instead of classic mean average [25]. Some advantages of the median averaging are that; it elicits hidden signals more clearly and it is not affected by infrequent large artifacts that much compared to mean averaging [25]. Özdamar and Kalayci [26] supported the advantages of median averaging over the conventional mean averaging in a study on the ABR signals. Median averaging is an efficient way to remove adverse effects of the outliers on the final averaged signal, yet it also removes the valuable data in the outliers causing significant loss of information [27, 28].

#### **3.2 Artifact handling methods for EEG**

Artifact avoidance, artifact rejection, manual rejection, automatic rejection, and artifact removal are the common methods to deal with artifacts [29]. Although it seems a simple solution to cancel EOG and EMG artifacts by instructing subject to avoid blinking or movement, it can result in change of amplitudes in evoked potentials as well as the additional cognitive load [29–31]. On the other hand, artifact rejection or manual rejection may require a person dedicated to this purpose of eliminating artifacts visually one by one in an EEG. Moreover, the artifact detection by an expert may be subjective, tedious, and time consuming. In addition, it can not be applicable to online removal [3]. However, automatic rejection can automate this artifact rejection procedure but it can eliminate non-artifact signals if not properly tuned. The automatic rejection of artifact containing EEG can depend on artifact amplitude based or EEG segment RMS based artifact detection and rejection. An example of a simple blink artifact removal is depicted in **Figure 3**. Since blinks have low frequency content compared to EEG, by low pass filtering, EEG can be reduced while blink artifact still remains at a high voltage level. Thus, an amplitude threshold based artifact rejection can be applied. As seen from **Figure 3**, red traces are the EEG and blue are the low pass filtered EEG signal. While a simple artifact rejection (without low pass filtering) using a threshold of 20 μV will produce false positives (red traces over 20 μV), in the low pass filtered EEG these false positives are prevented.

Usually one or two channels are dedicated to detect EOG artifacts. There are two widely used procedures for EOG artifacts, first EOG rejection where EEG trials with EOG artifacts having VEOG greater than a preset threshold are omitted, and second EOG correction where the effect of eye movement is tried to be removed from EEG [6].

Artifacts can distort the EEG in a way that the electrophysiologists or physicians can be misled in their clinical interpretation [32]. This makes artifact removal critical in the pre-processing phase prior to analysis. There are many methods to

**Figure 3.**

*Low pass filtering based EEG blink rejection. Red is raw EEG, blue is low pass filered EEG with 6th order Butteworth low pass filter at 8 Hz cut off. The detected artifact containing EEG epochs are shown in dashed rectangles.*

remove artifacts such as Artifactual Segment Rejection, Filtering, Wiener filtering, Adaptive Filtering, Time-Frequency Representation, Wavelet Transform, Discrete Wavelet Transform (DWT), Adaptive Noise Cancelation (ANC), Wavelet Packet Transform (WPT), Kalman Filtering, Linear Regression, Blind Source Separation (Principal Component Analysis (PCA), Independent Component Analysis (ICA), Canonical Correlation Analysis (CCA), Minor Components Analysis (MCA)), Source Decomposition, Empirical Mode Decomposition (EMD), Support Vector Machine (SVM), and hybrid methods [3, 4, 29, 33–38]. A functional dedicated artifact channel which provides complementary aid to identify ECG/EOG is required to remove ocular or cardiac artifacts in the most of the available methods [4].

Regression is a common and well established technique in artifact removal, yet it cannot be used to remove muscle noise or line noise, since these type of artifacts have no reference channels [39]. Having a good regressor (e.g., an EOG) is critical in both time and frequency domain regression methods. It is an inherent weakness that eye movements and EEG signals are bidirectional. When unacceptable amount of data are lost in artifact rejection, delicate artifact removal methods which will preserve the essential EEG signals while removing artifacts are necessary [39]. One of the most important artifacts is EOG. EEG regions infected with EOG can be rejected from overall EEG signal with simplest artifact rejection where these portions are detected by EOG channels, however these regions still carry brain signals in addition to ocular artifacts and total rejection or subtraction of EOG from them results in loss of brain data [40–42].

Blind Source Separation (BSS) algorithms utilize multiple channels in an unsupervised learning algorithm to extract brain related activity from the ensemble EEG signal which can be assumed a linear superposition of brain signals, noise and artifacts [38]. Three common BSS algorithms are Independent Component Analysis (ICA), Principal Component Analysis (PCA) and Canonical Correlation Analysis (CCA).

ICA, a BSS method, is often used to remove EEG artifacts based on statistical approach of spatial filtering and separation of multiple channel EEG data into spatially fixed and temporally independent components [39, 43, 44]. Since the EEG sources and artifacts are usually of different origins, they can be assumed to be linear summation of each independent components. ICA method finds these statistically independent components and enable us to eliminate artifactual ones

#### *A Brief Summary of EEG Artifact Handling DOI: http://dx.doi.org/10.5772/intechopen.99127*

from the desired EEG [45]. On the other hand, ICA provides extraction of the eye related signals present in the EOG, and removal of this information or artifact, rather than the complete EOG which still has some brain activity [40], is possible. However, detection and removal of transient artifacts such as head and neck muscle contractions and movement are difficult with ICA [46]. Moreover, adapting ICA as an online method requires high computational power [46]. On the other hand, an advantage of ICA is that it does not rely on a reference channel [39]. However, many artifact removal algorithms are compared in [3], and Revised Aligned-Artifact Average (RAAA) and Second Order Blind Identification (SOBI) and Adaptive Mixture of Independent Component Analyzers (AMICA) are the preferred artifact removal methods for EOG, EMG and ECG artifacts.

PCA uses orthogonal transform of correlated time domain signal into linearly uncorrelated principal components (PCs) [47]. These principal components possess as much as variance of the EEG as possible. Artifact containing PCs can be eliminated if they are uncorrelated with the brain EEG. Application of PCA into ocular artifacts was provided in [48].

CCA is also another method utilized in removing artifacts. In CCA second order statistics are employed, correlation between two multivariate datasets are maximized by canonical variables. CCA offers shorter computational time compared to ICA [38].

Another method is filtering in frequency domain. Usually a high-pass filter starting from 0.5-1 Hz is applied for baseline drift removal. Notch filters are used to remove powerline-noise. Another one, EMG activity of contracting scalp sites can hinder the signals of interest in the EEG recordings during an epileptic seizure [49]. It was possible to remove this high frequency content EMG activity from EEG spectra by filtering out signals over 25 Hz. Adaptive Filters, Wiener Filtering and Bayesian Filters are three filtering methods applied in EEG signal preprocessing. Adaptive Filters are the most commonly used for artifact removal [47]. In Adaptive Filtering a reference channel for artifacts is subtracted from the EEG recursively. This reference is multiplied by a weight factor obtained from the output of the filter by a learning algorithm and this weighted reference is subtracted from the recorded EEG yielding output artifact free EEG changing adaptively [50].

In wavelet transform, many scaled and time shifted wavelets are used to produce coefficients for the particular signal and wavelet type by convolution of the signal and wavelets. These coefficients indicate similarity between the corresponding wavelet and the signal. In artifact removal via wavelet transform, the main idea is that the signal which can be highly correlated with a basis mother wavelet and can be separated from artifacts which might have no correlation to the principal mother wavelet [50]. Some examples of Wavelet Transform in artifact removal are for ocular artifact removal as in [51, 52].

#### **3.3 EEG pre-processing pipelines available**

Recently many preprocessing pipelines have been introduced in order to reduce the burden of artifact handling by an expert one by one visual inspection. This laborious task can be fastened by using existing automatized preprocessing methods in order. An efficient pre-processing pipeline not only helps the artifact management time but also provides objective evaluation with predefined criteria compared to highly subjective artifact handling by a human expert. The preprocessing pipelines usually consist of the combination of the following stages; filtering, re-referencing, bad channel identification (and interpolation), bad channel and epoch removal, artifact detection using ICA, artifact correction and removal [53], see **Figure 4**.

**Figure 4.**

*APP artifact management flow diagram from [53].*

Fully Automated Statistical Thresholding for EEG artifact Rejection (FASTER) [54] algorithm is a state of the art method which is available in EEGLAB toolbox [55]. FASTER has filtering, line noise removal, bad channel detection and interpolation, segmentation, and artifact rejection on segments by identifying bad channels, blinks, eye movements and muscular artifacts using combination of statistical thresholding and ICA [56]. It requires an extra EOG channel. The Automatic Pre-processing Pipeline (APP) removes powerline noise, bad channels, eye movements, blinks and muscular artifacts using ICA to identify artifactual components [53], see **Figure 4**. However, it also requires extra EOG channels. Da Cruz et al. [53] has found that APP performs better than FASTER yielding higher amplitude in ERP study. Another pipeline is Tool for Automated Processing of EEG data (TAPEEG) [57]. It uses automated routines of FASTER and Fieldtrip for artifact identification and performed similar to visually analysis by an expert [58]. TAPEEG handles the resting state EEG data as well. Both FASTER and TAPEEG are based on z- scores and have difficulty in handling outliers, this leads to loss of signal content due to false positive artifact detection and rejections [53]. Another standardized

#### *A Brief Summary of EEG Artifact Handling DOI: http://dx.doi.org/10.5772/intechopen.99127*

preprocessing method for large EEG datasets, PREP pipeline, handles line noise removal, bad channel detection, and referencing to standardize and normalize the data before processing [58]. It is also available as plug-in in EEGLAB toolbox.

Automagic is a toolbox developed for standardized handling of large growing EEG/ERP datasets by time [56]. The power of Automagic comes from the fact that it exploits many existing pipelines and methods, such as PREP pipeline for bad channel identification and for average referencing, Cleanline [59] to remove power line noise, EOG regression [60], Multiple Artifact Rejection Algorithm (MARA), ICA or robust PCA for artifact correction [61]. MARA is a plug-in available in EEGLAB which automatically identifies artifacts not only ocular or muscular but also any general artifactual source component in ICA [61]. Pedroni et al. [59] showed that combination of a preprocessing pipeline to identify bad channels and MARA method is efficient to remove most of the artifacts.

None of the methods offers a perfect robust and high accurate management of all types of artifacts. In general, they are all limited with the training dataset and fail to achieve high success with new type of artifactual data.

#### **3.4 Simultaneous EEG and f-MRI artifact handling**

Since EEG is widely used as a clinical tool to monitor or diagnose patients, doctors can be misguided in case of artifacts and EEG can be misinterpreted. For this reason, artifact removal becomes a crucial point for some cases such as epilepsy monitoring in an EEG/fMRI recording room. Today EEG and fMRI are two distinct but closely related and complementary methods. While fMRI provides high spatial resolution for localization of phenomena in the brain, EEG on the other hand results in better temporal resolution [62–65]. One should be careful about the experiments involving both fMRI and EEG because there are many unwanted electromagnetic sources interfering with EEG. For example, the false identification of spikes are highly possible since residuals of Ballistocardiogram (BCG) artifacts have similar shapes as epileptic spikes [66]. The factors that can lead to differences in the artifact are linked to the subject and experimental setup, [67]. There are imaging artifacts, cardiac related Ballistocardiogram artifacts (BCG), EOG and EMG artifacts in an EEG inside MRI [44]. Static field (B0) and the time-varying fields of radio-frequency excitations and of imaging gradients, generate artifacts in the EEG known as Ballistocardiogram (BCG) and imaging artifacts [44, 68–70]. The pulse artifact which can be observed in EEGs recorded inside MR scanners easily, is due to a fundamental cause that any movement of electrically conductive muscles in a static magnetic field generates electromagnetic induction and it is proportional to the static field, generally larger at higher field strengths [67, 71]. Pulsations of the scalp arteries are the main cause of this type of BCG artifact [72, 73]. The study of Grouiller et al. [44] compared different imaging artifact removal techniques and various cardiac artifact correction techniques in both simulated EEG data and in real experimental data. They concluded that there is no key for every door, some algorithms work well for some case and others might work well for other cases. Certain algorithms may be preferred depending on the type of data and analysis method [44]. Another algorithm, adaptive Optimal Basis Set (aOBS), automatically eliminates BCG artifacts yet preserving the neural origin signals in EEG [74]. It can be used efficiently for simultaneous fMRI and EEG recordings.

#### **3.5 Sleep stage classification artifact handling**

Manual artifact detection is still the most common method for artifact handling for sleep stage classification, however, the long time required and the difficulty

to apply it to large datasets poses the main disadvantages [75]. Malafeev et al. [75] compared 12 simple algorithms that are applicable with a single EEG channel for ease of use. It was found that automatic artifact detection in EEG during sleep within large datasets is possible with simple algorithms. Among these, Power thresholding 25–90 Hz (PT25), Power thresholding 45–90 Hz (PT45) and Autoregressive (AR) models had Reciever Operating Characteristic (ROC) areas above 0.95. In addition, online detection is also possible with the majority of these simple algorithms.

#### **3.6 BCI Artifact handling**

Artifact removal in BCI applications are getting more attention. By studies it was shown that artifacts generated by EOG and EMG activities affect the neurological signals utilized in a BCI system [10, 76]. Although there are extensive researches into artifact removal for BCIs and developed efficient methods such as Fully Online and Automated Artifact Removal (FORCe), Lagged Auto-Manual Information Clustering (LAMIC), Fully Automated Statistical Thresholding for EEG artifact Rejection (FASTER) and K-Singular Value Decomposition (K-SVD), the field lacks an effective artifact removal [12, 54, 77–82]. The surrogate-based artifact removal (SuBAR) technique proposed by Chavez et al. [33] effectively cancels EOG and EMG artifacts from single-channel EEG. Chang et al. [83] proposed a method for detection of eye artifact from single prefrontal channel which is useful for headband-type wearable EEG devices with a few frontal EEG channels. Compared to conventional methods the accuracy of detecting ocular artifact contaminated epochs was significantly better. Daily-life EEG-BCIs are getting popular and artifact removal techniques for these BCIs must have some critical features such as; must be performed outdoor, with portable wearable wireless device, with real EEG signals, compatible with daily life tasks, must have simple electrical montage, must use dry electrodes, must remove complex artifacts, must work only EEG without reference, must work online and must work with single electrode channel. More research into artifact removal other than ocular and cardiac artifacts is necessary especially for those daily-life EEG BCIs [36].

While ICA and PCA are common artifact removal methods, Artifact Subspace Reconstruction (ASR), which is a powerful automated artifact removal method available for both online real-time and offline, can be applied to prevent transient and large artifact [46, 84]. It also does not require additional channel and cleans the data from artifacts.

#### **4. Conclusion**

The number of artifact handling techniques and algorithms are increasing drastically, however the artifact problem is still challenging for many applications. Particularly, the internal or physiologic artifacts are difficult to distinguish and remove. While simple measures such as artifact avoidance and artifact rejection can be utilized in some applications, most of the cases require special methods dedicated to handle artifacts in order to significantly reduce their harmful effects on signal of interest. Due to the varying nature of artifacts a generic method for all sorts of artifacts is still missing. However preprocessing pipelines provides some efficient approaches to this challenge. In future, the progress in machine learning and deep learning based approaches may yield more efficient, accurate and robust artifact removal options. Online artifact removal methods such as ASR must be developed to overcome various artifacts in daily life to be efficient for BCIs.

*A Brief Summary of EEG Artifact Handling DOI: http://dx.doi.org/10.5772/intechopen.99127*

### **Author details**

İbrahim Kaya Department of Biomedical Engineering, Izmir Katip Celebi University, Izmir, Turkey

\*Address all correspondence to: ibrahimkaya21@yahoo.com

© 2021 The Author(s). Licensee IntechOpen. This chapter is distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/ by/3.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

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Section 2
