Brain-computer interface (BCI) is a method of direct communication between the human brain and the computer without the need for the use of other physical organs such as peripheral muscles, where the interface allows the human to order any electronic device using brain activity only. Multiclass discrimination of motor imagery movements is one of the BCI applications that uses electrical signals measured from brain or muscles; these signals were filtered and processed using time, space, and frequency feature extraction techniques, and machine learning algorithms to construct a system achieve best performance. The objective of this research was to make a comparative study between different wavelet Daubechies and Coiflets families mother functions used as feature extraction to discriminate between 4 classes of imagined movements. The coefficients of different Wavelet Daubechies (2-20) and Coiflets (1-5) were examined in calculating common spatial filters to discriminate between 4 imagined classes, where 4 detail levels of the wavelet coefficients were calculated; support vector machine was used as a classifier that enables the classification of imagined movements. The algorithms applied to the BCI competition IV data set 2a. Result yields performance of 0.53 and 0.52 average κ coefficients using Db2 and Coif1, respectively.
Brain-computer interface (BCI) is now being one of the interesting and attractive topics for researchers in recent years. Common spatial pattern is the most common approach applied in motor imagery (MI)-based BCI because of its effectiveness in differentiating between 2 different MI classes. The main objective of this article is to review using common spatial pattern combining with different methods such as Hilbert transform, error correction output coding, wavelet transform, joint approximate diagonalization, and others to extract features that can be used to differentiate between multiclass MI-based BCI. Data set 2a from BCI competition IV is used as an example for all authors. In addition, results of using various classifiers are demonstrated.
Motor imagery (MI) based on electroencephalography (EEG) is one of the methods that the brain-computer interface (BCI) system uses to identify the expected behavior through brain signals. In this study, we aimed to develop an algorithm that is capable of differentiating between 4 MI movements. To achieve this, the Data Set IIa A from BCI competition IV was used to test the algorithm. We used independent component analysis (ICA) to preprocess the signal and wavelet technique to decompose the obtained signal into the desired frequency bands. We then inserted these as common spatial pattern (CSP) input, maximizing the variance between 2 classes using the 1-versus-1 (OVO) technique. Afterward, the support vector machine (SVM) classifier is used to obtain the best possible separation between the 2 classes. The obtained result shows improvement in some significant subjects compared with a previous study of these techniques.
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