The goal of this research involving a motor imagery brain-computer interface paradigm is to assess the possibility of enhancing the classification rate handling a feature vector based on the modulation of electrophysiological brain activity in specific bands. A new amplitude modulation energy index of the cerebral rhythms is proposed as feature vector concept. The method is proven on a public database and on a set of electroencephalographic data recorded in our own laboratory. In both cases, only eight electrodes are used in order to reach high performance classifying rates. The discrimination of motor tasks (imagination of right and left hand movements) is analyzed by means of five classifiers: support vector machine, k nearest neighbor, linear discriminant analysis, quadratic discriminant analysis and Mahalanobis distance based classifier. For our database, the medians of the classification rates for two of classifiers are very high (94.62 % -97.76 %) when some rhythms are modulated in theta and alpha bands. Significantly higher classification rates reported herein (greater than 90 % for both of the databases) compared with classifiers trained on the other features prove that the index may be very useful for highlighting the modulation found in certain bands of the EEG rhythms.

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Quantitative evaluation based on amplitude modulation analysis of electroencephalographic signals is proposed for a brain computer interface paradigm. The method allows characterization of the interaction effects of different frequency bands in the electroencephalographic rhythms during motor tasks. A new index was proposed and computed to be a measure of the amplitude modulation. Built on this index, features vector are established for training different classification algorithms. Signals recorded from 50 subjects revealed important differences in amplitude modulations between motor tasks. Most notably, Theta modulation of the Theta and Alpha rhythms proved to be reliable discriminant features between different mental tasks.

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Abstract-A comparative evaluation is performed on two databases using three feature extraction techniques and five classification methods for a motor imagery paradigm based on Mu rhythm. In order to extract the features from electroencephalographic signals, three methods are proposed: independent component analysis, Itakura distance and phase synchronization. The last one consists of: phase locking value, phase lag index and weighted phase lag index. The classification of the extracted features is performed using linear discriminant analysis, quadratic discriminant analysis, Mahalanobis distance based on classifier, the k-nearest neighbors and support vector machine. The aim of this comparison is to evaluate which feature extraction method and which classifier is more appropriate in a motor brain computer interface paradigm. The results suggest that the effectiveness of the feature extraction method depends on the classification method used.