Feature Classification of EEG Signal with Binary Heuristic Optimization Algorithms

In previous paper, we proposed the novel method of nonlinear unsupervised feature classification for EEG (Electroencephalography) signal based on HS (Harmony Search) algorithm. Using this method, we could convert classification problem into finding the smallest sum of Euclidean distances between vectors belonging to each class. Therefore the performance of proposed method was influenced by the performance of optimization algorithm. In this paper, to compare efficiency and performance of various heuristic algorithm for this method, we applied three different heuristic optimization algorithm, HS, PSO (Particle Swarm Optimization), and DS (Differential Search). For the simulation, we used EEG signal data from BCI Competition IV Dataset I. Two class data from two subject with 100 Hz sampling rate were used. For feature extraction, common spatial pattern algorithm was used. In conclusion, the fastest algorithm was HS algorithm with about 4. 4 seconds of an average computational time, the algorithm with best classification rate was also HS algorithm and the average classification rates of subject 'f' and 'g' were 84.08 % and 81.95 %. The slowest heuristic algorithm was PSO algorithm with about 7.5 second in an average computational time, and the worst average classification rate was 57.27 % from subject 'g' with PSO algorithm. We could draw a conclusion that the best algorithm for proposed classification method was HS algorithm.