Fractal dimension methods to determine optimum EEG electrode placement for concentration estimation

• Published in: Neural computing & applications Vol. 31; no. 3; pp. 945 - 953
• Main Authors: Siamaknejad, Hossein, Liew, Wei Shiung, Loo, Chu Kiong
• Format: Journal Article
• Language: English
• Published: London Springer London 14-03-2019; Springer Nature B.V
• Subjects: Algorithms; Artificial Intelligence; Computational Biology/Bioinformatics; Computational Science and Engineering; Computer Science; Data Mining and Knowledge Discovery; Electrodes; Fractal analysis; Fractal geometry; Fractals; Image Processing and Computer Vision; Original Article; Placement; Position measurement; Probability and Statistics in Computer Science; Medical signals; Fractal dimensions; Electroencephalogram; Attention estimation
• ISSN: 0941-0643; 1433-3058
• DOI: 10.1007/s00521-017-3126-1

In this study, fractal dimension approaches were used for analyzing EEG to determine the optimum electrode placement to distinguish between concentration and relaxation states. The EEG of participants was recorded from multiple electrode placements while under relaxed state and while in a state of heightened mental activity. Higuchi and Katz algorithms were applied to extract the fractal dimension FD indices of windowed segments of the EEG. These were then plotted in a graph, and a simple threshold was applied to best divide the indices of relaxation and concentration. The Higuchi algorithm was found to be better than Katz at distinguishing between relaxation and concentration, and P3 was found to be the best position to measure concentration, with P8 being a close contender.