**1. Introduction**

The brain computer interface (BCI) technology makes the possible manipulation of embedded systems using signals generated by brainwaves. A characteristic of the BCI system can easily capture brain signals generated by neural activities, it can also recognize differently firing neural activity patterns, and these signals can transform them into useful commands [1]. These commands can be utilized to control the machines or the devices. BCIs are most commonly applied in prosthetic limbs for paralyzed patients, exoskeletons, robotics, autonomous vehicles, virtual keyboard and computer games [2]. The BCI system can be classified as invasive, non-invasive (these are classified based on the location of placement of EEG biosensors). Non-invasive BCIs are based on electroencephalography (EEG) to record the brain activities using a series of biosensors disposed on the scalp will be able to measure the potential generated by the electrical activity of thousands to billions of cortical neurons inside our brain [3]. Our study is focused on noninvasive BCI using an Electroencephalogram. The neocortex is a convoluted surface which resides at the top of the brain. It is about ⅛ of 1 inch thick. It has 30 billion neurons arranged in 6 layers. Each neuron makes around 10,000 synapses with other neurons, which results in around 300 trillion connections in the total [4]. The most common type of neuron in the cortex is the pyramidal neuron, populations of which are arranged in columns oriented perpendicular to the cortical surface. The surface of the cortex is convoluted, with Fissures sulci, Ridges gyri. The neocortex exhibits functional specialization. Each area of the cortex is specialized for a particular function. The occipital areas near the back of the head

specialize in basic visual processing [5]. The parietal areas towards the top of the head specialize in spatial reasoning and motion processing [6]. Visual and auditory recognition occurs in the temporal areas (towards the sides of the head) while frontal areas are involved in planning and higher cognitive functions. Inputs to a cortical area mainly come into the middle layers, Outputs of the cortical area leave from the upper and lower layers [7]. Based on these input–output patterns, the cortex roughly acts as an organized network of sensory, motor areas. Coming to the EEG, it is a device that extracts, organizes, and filters the electric signals which exist due to the neural firings (action potential) of the brain it is used for various diagnosing purposes, it is a popular non-invasive technique for recording the neuronal firing using electrodes placed on the scalp. The currents originating deep in the brain due to the firing of the neurons are not detected by EEG because the voltage fields will fall off with the square of the distance from the source [8]. The time domain signal displays the signals from different electrodes in a graph known as electroencephalography. EEG will reflect the summation of postsynaptic potentials occurring due to firing of thousands of neurons which are oriented radially to the scalp but not due to tangential electrodes. The spatial resolution of EEG is poor in a square centimeter range because of the impedance caused due to the presence of skull, scalp, CSF, meanings [9]. These layers' act as volume conductors and low pass filters to smear the original signals, whereas coming to the temporal resolution is good at the range of milliseconds [9]. This time domain signal from EEG is then converted into frequency domain signal using different transforms in the signal processing such as discrete Fourier transform, fast Fourier transform, etc. The amplified frequencies (according to the fast Fourier transform) which are extracted from brain by electroencephalogram into 4 ranges they are theta (θ) which ranges from 4 Hz to 8 Hz, alpha (α) which ranges from 8 to 12, beta (β) ranges from 12 to 25 Hz and finally gamma (γ) ranges minimum from 25 Hz to maximum of 45 to 75 Hz [10]. After performing many experiments on many patients specifically to observe the type of waves and the amplified frequencies (when will they occur, in what state of patient these waves can be observed) they have presented a generalized form of relation between their frequency ranges and normal human functions. When a person is ready or about to perform tasks or if he/she is in an alert state then more percentage of α frequency waves are generally observed and if a person is task oriented or if he is in a busy state or anxiously thinking or actively concentrating then high percentages of β frequency waves are generally observed, if a person is performing high motor functions, or if the person is switching the activities during multitasking then high percentage of γ frequency waves are observed mostly in the frontal lobe of the human brain. After performing several tests, I was able to predict that in my meditation state a high percentage of θ frequency waves were observed even in a sleeping state where the mind is in a relaxed condition there are high percentages of θ frequency waves. The Emotiv Insight is an EEG Brain wear device which is composed of five sensors that are projected to acquire and measure the key activity from the entire functional areas of the cortex. The device can provide raw EEG Signals, Mental Commands (conscious thoughts), Facial Expressions - Facial mimicry and Measurements of performance of the brain. The principal key characteristics of this scientific design is the dynamic brain-computer interface interactions with more degrees of freedom for controlling physical and virtual objects. The device accurately identifies mental states and emotion such as Engagement, Focus, Excitement, Meditation, Relaxation, Stress [11]. There is a possibility to build brain activity models in real-time based on spatial resolution. A deeper perspective on specific patterns of an individual's brain activity. The very important problem in EEG

#### *Brain Computer Interface Drone DOI: http://dx.doi.org/10.5772/intechopen.97558*

processing is low signal to the noise ratio since there are many layers between the neural cortex and the scalp and also due to the artifacts which result with great amplitude, the solution to minimize the noise is we can use filtering techniques and noise reduction techniques to remove the noise from the raw EEG data and extract the brain activity signals [12]. Since the EEG signal is non-stationary signal we use classifiers which are trained on user data (which is limited, this is also another main problem) we can generalize those results poorly to the already trained data on the same individual (different for different individuals because of physiological differences, this also limits the use of EEG applications) at different times. The accuracy might increase as we increase the number of training sessions but generalizing for subjects, i.e., to handle inter-subject variability processing pipelines with domainspecific approaches are used in order to clean, extract relevant features and classify (Riemannian geometry based classifiers, adaptive classifiers) EEG data. The subset of Machine Learning which is Deep Learning is used mainly to extract the features, Recently CNNs (convolutional neural networks) are used to simultaneously extract the feature and the classifier in order to achieve end to end supervised feature learning. Hence the devices use CNNs and recurrent neural networks of 3 to 10 layers in total [13].
