Deep Learning Algorithm for Brain-Computer Interface

Table 2

Comparison of classification algorithms.

Title	Algorithm	Input features	Efficiency	Advantages	Drawbacks/tradeoff
Vidaurre et al. [12]. Toward unsupervised adaptation of LDA for brain-computer interfaces. IEEE transactions on biomedical engineering, 587–597.	Linear discriminant analysis uses hyperplanes for different classes, assuming normal distribution, with equal covariance matrix for both classes; to solve an NC class problem, several hyperplanes are used.	Separating hyperplane is obtained by seeking the projection that maximizes the distance between two classes’ means and minimizes the interclass variance.	Suitable for online BCI and provides generally good result, and fluctuations in the training data set do not affect much.	Very low computational requirement so suitable for online BCI system.	Linearity that can provide poor results on complex nonlinear EEG data (not immune to noise).
Li et al. [5]. Classification of epilepsy EEG signals using DWT-based envelope analysis and neural network ensemble. Biomedical signal processing and control, 357–365.	Support vector machine uses a support vector hyperplane to identify classes.	Selected hyperplane is the one that maximizes margins, i.e., the distance from nearest training points.	Enables classification using linear decision boundaries, (linear SVM) has been applied, generalization capabilities, to be insensitive to overtraining and to the curse of dimensionality.	Maximizing margins and regularization are known to increase the accuracy.	These advantages are gained at the expense of a low speed of execution.
Lotte et al. [4]. A review of classification algorithms for EEG-based brain-computer interfaces: a 10-year update. Journal of neural engineering, 031005.	Neural network consists of at least three layers of nodes. Except for the input nodes, each node is a neuron that uses a nonlinear activation function which utilizes a supervised learning technique called backpropagation for training.	Neurons of the output layer determine the class of the input feature vector.	Applied to almost all BCI applications.	Because universal approximators are composed of enough neurons and hidden layers, they can approximate and classify any continuous signal.	Sensitive to overtraining, especially with such noisy and nonstationary data as EEG; therefore, careful architecture selection and regularization is required.
Usakli [1]. Improvement of EEG signal acquisition: An electrical aspect for state of the art of front end. Computational intelligence and neuroscience.	Consists of the noninvasive technique for recording brain signals which is based on the electromagnetic resonance signals compared to that of the EEG scalp signals.	Brain signals as an electrical pulse coming from the brain.	Suitable for all BCI systems. They are based on the noninvasive technique in real-time monitoring of signals.	Proven helpful for users and design engineers. One of the most important considerations is selecting suitable electrodes and headset.	Costly design because of the gold electrodes. They are costly, and everyone cannot afford that system.
Alzahrani [2]. P300 wave detection using Emotive Epoc+ headset: Effects of matrix size, flash duration,	Emotive Epoc+ 14 channel sensor was used which has 14 channels for EEG and a neutral channel as well.	Input features consist of P300 steady-state evoked potentials.	Suitable for the brain signals which are collected by the Emotive Epoc+ sensor. The signals are carried out using Emotive Epoc.	Advantage is basically being optimized, and the device is very cheap in price with affordable accuracy.	For more number of class predictions, the accuracy becomes low, and the output is affected.
Ayoubian, L. a. (2013). Automatic seizure detection in SEEG using high frequency activities in wavelet domain. Medical engineering and physics, 35, 319–328.	Based on the continuous wavelet transform, the brain signals were computed by convolving the SEEG signal.	Brain signals were collected from the Stellate Harmonie system for EEG monitoring purpose. These signals were passed through a band-pass filter.	Suitable for the detection of the seizure. A seizure onset is added to the signals and then compared with the normal brain signal.	For automatic seizure detection, it is very useful, and it can be used for the patients who are not able to calculate when they have seizure.	The disadvantage is basically for some high-frequency seizures. The high-frequency seizures are not detected easily because of the band-pass filter.
Liu et al. [6]. Feature selection for motor imagery EEG classification based on firefly algorithm and learning automata. Sensors.	Spectral regression discriminant analysis (SRDA) is widely used in the feature classification; in this paper, they have implemented this algorithm.	Separating hyperplane is obtained by seeking the projection that maximizes distance between two classes’ means and minimizes the interclass variance.	Suitable for online BCI and provides generally good result, and fluctuations in the training data set do not affect much.	Very low computational requirement so suitable for online BCI system, simple to use, and generally provides good results.	Linearity that can provide poor results on complex nonlinear EEG data (not immune to noise).
Lowne et al. [9]. Sequential non-stationary dynamic classification with sparse feedback. Pattern recognition, 897--905.	Spectral regression discriminant analysis (SRDA) is widely used in the feature classification; in this paper, they have implemented this algorithm.	Separating hyperplane is obtained by seeking the projection that maximizes distance between two classes’ means and minimizes the interclass variance.	Suitable for online BCI and provides generally good result, and fluctuations in the training data set do not affect much.	Very low computational requirement so suitable for online BCI system, simple to use, and generally provides good results.	Linearity that can provide poor results on complex nonlinear EEG data (not immune to noise).
Liu et al. [6]. Unsupervised adaptation of electroencephalogram signal processing based on fuzzy C-means algorithm. International journal of adaptive control and signal processing.	Common spectral patterns were used for the feature extraction and the linear discriminant analysis, and fuzzy C-means was used for the feature classification.	The maximum distance was calculated for each fuzzy C-means, and then the mean was calculated; after that, the features were classified.	They are suitable for nonlinear EEG signals having different amplitudes for different people.	Very low computational requirement so suitable for online BCI system, simple to use, and generally provides good results.	Fuzzy behavior can be seen in the output when the frequency changes at the input.
Pfurtscheller and Neuper [10]. Motor imagery and direct brain-computer communication. Proceedings of the IEEE, 1123–1134.	Hidden Markov model was used for the classification of EEG signals as they are nonlinear in nature, so we can tune the Markov model accordingly.	The input signals were obtained from the two channels, and these signals were transformed into the HMM network.	For two channels, EEG signals, this is good, and it has a fast classification.	The method used in this paper uses low computational power, and the model functions are optimized.	Output accuracy depends on the linear behavior of the signals. When the frequency fluctuates the output, control will also change.