Abstract
Brain-Computer Interfaces (BCIs) have become a research field with interesting applications, and it can be inferred from published papers that different persons activate different parts of the brain to perform the same action. This paper presents a personalized interface design method, for electroencephalogram- (EEG-) based BCIs, based on channel selection. We describe a novel two-step method in which firstly a computationally inexpensive greedy algorithm finds an adequate search range; and, then, an Estimation of Distribution Algorithm (EDA) is applied in the reduced range to obtain the optimal channel subset. The use of the EDA allows us to select the most interacting channels subset, removing the irrelevant and noisy ones, thus selecting the most discriminative subset of channels for each user improving accuracy. The method is tested on the IIIa dataset from the BCI competition III. Experimental results show that the resulting channel subset is consistent with motor-imaginary-related neurophysiological principles and, on the other hand, optimizes performance reducing the number of channels.
Highlights
Recent advances in Cognitive Neuroscience and Brain Imaging technologies provide us with the ability to interact directly with the human brain, offering an alternative to natural communication and control
This paper focuses on subject-dependent channel selection, and its main goal is the automatic selection of a reduced number of channels adapted to each subject, maintaining, or even improving, the classification accuracy in EEG-based BrainComputer Interfaces (BCIs)
In this paper a two-step system for channel selection by means of Estimation of Distribution Algorithm (EDA) has been presented, aiming at maintaining or even improving the classification accuracy with a few EEG channels automatically selected for each subject
Summary
Recent advances in Cognitive Neuroscience and Brain Imaging technologies provide us with the ability to interact directly with the human brain, offering an alternative to natural communication and control. The aim of a BCI system is to establish a communication method that translates human intentions, mental tasks reflected by suitable brain signals (e.g., electric, chemical, and blood flow changes), into a control signal for an output device such as a computer application or a neuroprosthesis, not requiring any muscular response. The idea is to provide a new communication method to people who are paralysed but are cognitively intact. Motor-Imaginary BCI systems are based on the fact that imagination of movement changes brain activity in the cortex. The recognition of patterns associated with certain movements could be used to generate control signals
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