Using Higher Order Nonlinear Operators for SVM Classification of EEG Data

摘要

Brain-computer interface (BCI) is a communication system that translates brain activity into commands for a computer or other digital devices [1]. The major goal of BCI research is to develop systems that allow disabled users to communicate with other persons, to control artificial limbs, or to control their environment. Other applications include multimedia communication, Augmented Reality applications, robot control and game development. The majority of BCI systems work by reading and interpreting cortically evoked electro-potentials across the scalp via an electro-encephalogram (EEG). The EEG signal has become the main data source of BCI study due to its low cost and non-invasive nature. The EEG data is inherently complex and difficult to analyze. Oscillatory activity in the EEG is classified into different frequency bands or rhythms: delta (0.5-3.5 Hz), theta (4-8 Hz), alpha 1 (8-10.5 Hz), alpha 2 (10.5-13 Hz), beta 1 (13-21 Hz), beta 2 (20-32 Hz), and gamma (36-44 Hz) [1]. Because EEG signals are non-stationary and nonlinear, and normally interfered by eye movements and muscle noises, it is difficult to differentiate the classes of mental tasks from EEG [2]. Different features can be extracted from the EEG data such as: time domain features related to changes in the amplitude of neurophysiologic signals, occurring time-locked to the presentation of stimuli or time-locked to actions of the user of a BCI, frequency domain features related to changes in oscillatory activity, and spatial domain features extracted and combined from several electrodes [1].