**2. Hybrid BCI and modes of operation**

MI detection is challenging due to low signal-to-noise ratio, but development of advance signal processing enables MI-based BCI to implement various tasks [33]. MI-based BCI was used first time by [34] for stroke rehabilitation in a tetraplegic patient using imagination of foot

MI-based BCI is a system that is subject specific and requires data recording and a system training for each new user. Subject-independent MI was developed by training the data acquired from several subjects [35] and a conscious target strengthens ERD in β frequency band [36]. ERD amplitude was higher due to body ownership illusion like moving rubber

MI activity acts as a neurofeedback and a feasible part of stroke rehabilitation but may increase moderate fatigue due to external factors like long hours of training session [38]. Neural plasticity can be achieved through neurofeedback [38, 39]. MI-based BCI uses a neurofeedback strategy in poststroke rehabilitation using functional electrical stimulation (FES), robot, and orthosis [40]. Majority of stroke patients can use EEG-based MI [41, 42] for limb rehabilitation [43] and was extended to imagination of tongue movement [44]. MI can be used for a reliable and high performance BCI for both healthy subjects and ALS patients where the user requires less trainings [45]. MI-based BCI can be used for stroke rehabilitation to perform various functions such as controlling computer cursor, processing word, accessing Internet, and controlling environment and entertainment [33]. Without any muscular activities, MI tasks were employed in an experiment to drive a car in 3-D virtual environment [46] and to play video

There are other methods apart from EEG to measure brain activities such as magnetoencephalography (MEG), electro-corticography (ECoG), functional magnetic resonance imaging (fMRI), and functional near-infrared imaging (fNIR). However, due to noninvasive method, easy experimental setup, low cost, and high efficiency, EEG is most widely used. Although P300, SSVEP, and ERD/ERS are most widely used EEG signals, there are also other brain signals such as slow cortical potentials (SCP) and electrooculogram (EOG) in BCI [29]. Each of these brain signals do not work same for all users. So, a novel approach has been used to combine two or more conventional BCIs to form a hybrid BCI to enhance the overall performance [48].

movement where the patient was able to grasp cylinder with the paralyzed hand.

**Figure 3.** Two states of "Hex-O-Spell" paradigm selecting a character using MI [31].

hand than other visual targets [37].

118 Evolving BCI Therapy - Engaging Brain State Dynamics

game on virtual ground [47].

The initial concept of hybrid BCI was used in [49] to incorporate electrocardiogram (ECG) with EEG for autonomous BCI switch ON and OFF operation to analyze whether heart bit rate can be used as an additional communication channel in BCI. P300 was combined to μ and β rhythms from sensorimotor cortex to operate a brain-controlled wheelchair [50]. In [51], hybrid P300/ SSVEP system was compared with conventional P300 and SSVEP BCI from 10 healthy subjects and observed improved performance relative to single SSVEP system and the user acceptability was higher for the hybrid which suggested the need for efficient future protocols. A continuous simultaneous hybrid BCI for two dimensional cursor control was introduced in [52] using ERD and SSVEP activity, in which vertical position of the cursor was controlled via ERD with imagined movement and the horizontal position with SSVEP from visual attention, and the overall result suggested that further research is needed to optimize hybrid BCI.

In [53], hybrid BCI systems were reviewed and different possibilities to combine their advantages and disadvantages were discussed. Hybrid P300/SSVEP was used by [54] for GO/STOP command in wheelchair control at simultaneous asynchronous mode and obtained improved performance in terms of detection accuracy and response time. A novel hybrid P300/SSVEP was designed by [55] integrating random flashing and periodic flickering to reduce adjacency problem and habitual repetition, and obtained an online classification accuracy of 93.85% and information transfer rate of 56.44 bit/min from 12 healthy subjects in a single trial. A new hybrid P300/SSVEP was proposed in [56] based on visual approach of shape changing instead of existing color changing and compared the performances with traditional P300, SSVEP, and normal P300/SSVEP hybrid. The new hybrid BCI was compared with normal hybrid and each traditional BCIs, and found better performance with 100% accuracy and 30 bit/min ITR for eight trials with 10 healthy subjects.

A systematic review of hybrid BCI was done by [57] in terms of taxonomy and usability. This review discussed two modes of operation: simultaneous and sequential modes. In simultaneous mode, any two BCI systems (e.g., P300 and SSVEP) work simultaneously controlling two functions at a time and this combined system might achieve higher accuracy and ITR. As explained previously in [52], the hybrid BCI used simultaneous mode which includes ERD (imagined movement) to control the cursor in vertical position and SSVEP to control the cursor in horizontal position. In sequential mode, output of one BCI system is used as the input for another to control various functions of the second BCI system or as a switch in asynchronous mode [57]. These two modes are depicted in **Figure 4a** and **b**.

Among all other EEG signals, SSVEP possess a better suitability to combine with P300 [58] for constructing efficient hybrid BCI due to the following reasons [55]:


**Figure 4.** Hybrid BCI modes of operation [57].

**3. Hybrid BCI classification**

elbow functions as per instructions.

**3.1. SSVEP-MI hybrid**

various parameters are illustrated in **Table 1**.

**Figure 6.** Hybrid BCI papers in IEEE and PubMed together.

The most common signals for BCI are P300, SSVEP, and ERD, and there are various approaches used to combine two or more these signals to make a hybrid to enhance performance. The most common methods for hybrid BCI are discussed below and their classifications based on

Hybrid Brain-Computer Interface Systems: Approaches, Features, and Trends

http://dx.doi.org/10.5772/intechopen.75132

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SSVEP and ERD signals are used to form a BCI hybrid that combines visual attention and motor imagination. In [61], 14 healthy subjects (six women and eight men of ages 17–31 years) were chosen to perform three different tasks: MI only (ERD signals generated from left-hand or right-hand movement), SSVEP only (visual signals generated from two flickering LEDs at 8 Hz and 13 Hz), and simultaneous hybrid SSVEP-MI. Linear discriminant classifier was used and the classification accuracy was higher for SSVEP than MI and was highest for the hybrid. An artificial upper limb was controlled by [62] combining SSVEP and MI in two degrees of freedom (DoF) in which MI controlled grasp function and SSVEP controlled elbow function (flexion and extension) of the limb. The experiment was conducted with 12 healthy subjects (7 male and 5 female) in offline and 7 healthy subjects (4 male and 3 female) in online. In offline experiment, 4 runs each with 40 trials were taken and the subjects were instructed to imagine feet dorsiflexion from two to four runs focusing the two flickering lights 7 and 13 Hz. The online experiment consisted a 2 DoF artificial upper limb and subjects controlled grasp and

In [63], SSVEP-MI hybrid was proposed to control the speed (accelerate or deaccelerate the wheelchair based on flashing stimuli of 7, 8, 9, and 11 Hz) and direction (left- and righthand imageries to control the direction) of a wheelchair in real time. Both virtual and realtime tests were conducted to observe the performance. Three options: straight driving, left and right turns were provided for direction, and accelerate, deaccelerate, or drive options for speed control using eight separate commands: turn left, turn right, drive forward, accelerate,

**Figure 5.** BCI and hybrid BCI papers in IEEE and PubMed.


The research on hybrid BCI is growing and **Figures 5** and **6** illustrate number of publications in this field. The two figures are based on searches at IEEE Xplore [59] and PubMed [60] with keywords: "BCI," "Hybrid BCI," "SSVEP and MI," "P300 and SSVEP," and "P300 and MI "published in Conference, Journals, Magazines, Books, and e-books in the fields of "Engineering," "Psychology," "Neuroscience," "Medicine," and "Computer Science."

**Figures 5** and **6** illustrate the number of published articles in IEEE Xplore and PubMed which are added together. They depict the growing numbers of research in hybrid BCI and among hybrid BCIs, number of P300 and SSVEP hybrid is comparatively higher as illustrated in **Figure 6**.

**Figure 6.** Hybrid BCI papers in IEEE and PubMed together.
