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Title | Algorithm | Input features | Efficiency | Advantages | Drawbacks/tradeoff |
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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). |
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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. |
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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. |
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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. |
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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. |
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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. |
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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). |
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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). |
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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. |
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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. |
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