Feature Selection of Multichannel EEG for Attention Classification
Received: 21 July 2025 | Revised: 17 September 2025 and 6 October 2025 | Accepted: 9 October 2025 | Online: 9 February 2026
Corresponding author: Atmoko Nugroho
Abstract
EEG (electroencephalography) is a tool to determine human brain waves and the function of the human brain. A complete EEG device consists of 33 channels and is expensive for independent research. This study aimed to determine which EEG features play an important role in attention and meditation. Data were obtained from 83 respondents with 8 channels and 5 waves in each channel. This study examines three feature selection methods. Using Random Forest, selecting the Fp1_beta, Fp1_gamma, Fp2_beta, Fp2_gamma, Fz_beta, Fz_gamma, C4_beta, C4_gamma, O1_beta, O1_gamma, O2_alpha, O2_beta, O2_gamma features led to an accuracy of 98.2%. Selecting the Fp1_beta, Fp1_gamma, Fp2_beta, Fp2_gamma, Fz_delta, Fz_alpha, Fz_beta, Fz_gamma, C4_beta, O1_beta, O1_gamma, O2_alpha, O2_beta, O2_gamma features with a tree-based model led to an accuracy of 98.0%. Finally, with recursive feature elimination, the Fp1_beta, Fp2_beta, Fz_beta, O2_beta, and O2_gamma features led to an accuracy of 96.7%.
Keywords:
EEG channel, feature selection, machine learningDownloads
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Copyright (c) 2026 Atmoko Nugroho, Danny Manongga, Hindriyanto Dwi Purnomo, Hendry Hendry
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