Analyzing electroencephalography (EEG) signals with machine learning approaches has become an attractive research domain for linking the brain to the outside world to establish communication in the name of the Brain-Computer Interface (BCI). Many researchers have been working on developing successful motor imagery (MI)-based BCI systems. However, they still face challenges in producing better performance with them because of the irrelevant features and high computational complexity. Selecting discriminative and relevant features to overcome the existing issues is crucial. In our proposed work, different feature selection algorithms have been studied to reduce the dimension of multiband feature space to improve MI task classification performance. In the procedure, we first decomposed the MI-based EEG signal into four sets of the narrowband signal. Then a common spatial pattern (CSP) approach was employed for each narrowband to extract and combine effective features, producing a high-dimensional feature vector. Three feature selection approaches, named correlation-based feature selection (CFS), minimum redundancy and maximum relevance (mRMR), and multi-subspace randomization and collaboration-based unsupervised feature selection (SRCFS), were used in this study to select the relevant and effective features for improving classification accuracy. Among them, the SRCFS feature selection approach demonstrated outstanding performance for MI classification compared to other schemes. The SRCFS is based on the multiple k-nearest neighbour graphs method for learning feature weight based on the Laplacian score and then discarding the irrelevant features based on the weight value, reducing the feature dimension. Finally, the selected features are fed into the support vector machines (SVM), linear discriminative analysis (LDA), and multi-layer perceptron (MLP) for classification. The proposed model is evaluated with two benchmark datasets, namely BCI Competition III dataset IVA and dataset IIIB, which are publicly available and mainly used to recognize the MI tasks. The LDA classifier with the SRCFS feature selection algorithm exhibits better performance. It proves the superiority of our proposed study compared to the other state-of-the-art BCI-based MI task classification systems.

Brain-computer interface (BCI) refers to the recognition of brain activity leading to generate corresponding commands to interact with external devices. Due to its safety and high time resolution, electroencephalogram (EEG) based BCIs have become popular. Steady-state visual evoked potential (SSVEP) is an EEG particularly attractive due to high signal to noise ratio (SNR) and robustness. A spatio-spectral feature fusion approach is studied to recognize the frequency of short-time SSVEP using correlated component analysis (CORRCA). Two reference signals are generated by averaging each half of the training trials. The signal of each channel is passed through a filterbank designed to decompose into a predefined set of subbands. The spatial correlation coefficients are calculated between each subband of the test trial and the reference signals using CORRCA. The two sets of coefficients derived from two reference signals are merged and sorted in descending order. Thus obtained coefficients are weighted using a nonlinear function to define their contribution in frequency recognition. The weighted coefficients are fused to obtain a single coefficient for the target stimulus frequency of individual subband. The derived coefficients for each subband are weighted with another nonlinear function and fused to single coefficient for the target stimulus. A similar process is applied for each stimulus frequency and then the frequency corresponding to the highest coefficient is recognized as the target stimulus. The performance of the proposed method outperforms other existing algorithms to recognize the stimulus frequencies of SSVEP.INDEX TERMS Brain-computer interface (BCI), correlated component analysis (CORRCA), electroencephalogram (EEG), feature fusion framework, filterbank analysis, steady-state visual evoked potential (SSVEP).

Automatic recognition of human emotion has become an interesting topic among braincomputer interface (BCI) researchers. Emotion is one of the most fundamental features of a human subject. With proper analysis of emotion, the inner state of a human subject can be assessed directly. The human brain response can be competently represented by electroencephalography (EEG). The selection of potential features in EEG related to human emotion is a very important task for developing an effective emotion recognition system. In this paper, the discriminative features computed from rhythmic components of EEG are used to recognize human emotional states. The narrowband rhythmic components theta, alpha, beta, and gamma are extracted from multichannel EEG signals using filter bank implementation. The short-time entropy and energy features are extracted from each of the rhythmic components. The spatial filtering has been performed on the entropy-energy space by using common spatial pattern (CSP). Thus obtained spatial features are employed to recognize the emotion states using support vector machine (SVM) classifier. The publicly available two datasets DEAP and SEED are used to evaluate the performance of the proposed method. The experimental results reflect that higher recognition accuracy is obtained by using higher frequency subbands (beta and gamma) than that of the lower frequency subbands (theta and alpha). The combination of features from all subbands has better performance than the features obtained from individual subband signals. The performance of the proposed method outperforms the recently developed algorithms of emotion recognition.INDEX TERMS Brain-computer interface (BCI), common spatial pattern (CSP), electroencephalography (EEG), emotion recognition, subband decomposition.