EEG-based Sleep Deprivation Classification: A Performance Analysis of Channel Selection on Classifier Accuracy
DOI:
https://doi.org/10.31436/ijpcc.v10i2.486Keywords:
electroencephalogram (EEG), classification, sleep deprivationAbstract
This study analyses the effect of electroencephalogram (EEG) channel selection on the classification accuracy of sleep deprivation using four distinct classifiers: Random Forest (RF), k-Nearest Neighbours (k-NN), Support Vector Machine (SVM), and Artificial Neural Network (ANN). In this study, the EEG data from ten male individuals in good health were collected. Two distinct sets of EEG channels—a limited frontal channel set (Fp1, Fp2) and a thorough 19-channel set—were used to compare the performance of the classifiers. According to our findings, the k-NN classifier produced the greatest classification accuracy of 99.7% when applied to the 19-channel EEG signals. In contrast, both SVM and ANN classifiers were able to obtain the greatest accuracy of 94% with the frontal channels. Though there are not many gaps, these results imply that employing a larger range of EEG channels greatly improves the classification accuracy of sleep deprivation. The present study emphasizes the significance of channel selection in EEG-based sleep deprivation investigations by showcasing the significant advantages of full EEG signal capture over minimum channel configurations.
References
J. C. Lo and M. W. Chee, “Cognitive effects of multi-night adolescent sleep restriction: current data and future possibilities,” Current Opinion in Behavioral Sciences, vol. 33, pp. 34–41, Jun. 2020, doi: https://doi.org/10.1016/j.cobeha.2019.12.005.
Khoo, Sing Yee, et al. "Resting-state electroencephalography (EEG) microstates of healthy individuals following mild sleep deprivation." Scientific Reports 14.1 (2024): 16820.
Y. Jeon, S. Jo, and H. Kim, "Sleep Stage Classification Using Multiple EEG Channels: A Machine Learning Approach," IEEE Transactions on Biomedical Engineering, vol. 66, no. 6, pp. 1320-1323, June 2019.
E. Eldele et al., "An Attention-Based Deep Learning Approach for Sleep Stage Classification With Single-Channel EEG," in IEEE Transactions on Neural Systems and Rehabilitation Engineering, vol. 29, pp. 809-818, 2021, doi: 10.1109/TNSRE.2021.3076234.
M. Kamrunnahar, T. Ahmed, and F. Rabbi, "Channel Selection Algorithms for EEG Signal Processing: A Review," EURASIP Journal on Advances in Signal Processing, vol. 2015, no. 1, pp. 1-19, Dec. 2015.
T. Alotaiby, F. E. A. El-Samie, S. A. Alshebeili, and I. Ahmad, “A review of channel selection algorithms for EEG signal processing,” EURASIP Journal on Advances in Signal Processing, vol. 2015, no. 1, Aug. 2015, doi: https://doi.org/10.1186/s13634-015-0251-9.
Sen, B.; Peker, M.; Çavu¸so? glu, A.; Çelebi, F.V. A comparative study on classification of sleep stage based on EEG signals using feature selection and classification algorithms. J. Med. Syst. 2014, 38, 1–21.
Sase, Takumi, and Marini Othman. "Prediction of ADHD from a Small Dataset Using an Adaptive EEG Theta/Beta Ratio and PCA Feature Extraction." International Conference on Soft Computing and Data Mining. Cham: Springer International Publishing, 2022.
Upadhyay, Prabhat Kumar, and Chetna Nagpal. "Wavelet based performance analysis of SVM and RBF kernel for classifying stress conditions of sleep EEG." Science and Technology 23.3 (2020): 292-310.
Liu, Junbiao, et al. "Automatic sleep staging algorithm based on random forest and hidden Markov model." Computer Modeling in Engineering & Sciences 123.1 (2020): 401-426.
Kamaruzzaman, Muhammad ‘Afiq Ammar, et al. "EEG Features for Driver’s Mental Fatigue Detection: A Preliminary Work." International Journal on Perceptive and Cognitive Computing 9.1 (2023): 88-94.
M. Fu et al., “Deep Learning in Automatic Sleep Staging With a Single Channel Electroencephalography,” Frontiers in Physiology, vol. 12, Mar. 2021, doi: https://doi.org/10.3389/fphys.2021.628502.
M. Klug and K. Gramann, “Identifying key factors for improving ICA?based decomposition of EEG data in mobile and stationary experiments,” European Journal of Neuroscience, vol. 54, no. 12, pp. 8406–8420, Oct. 2020, doi: https://doi.org/10.1111/ejn.14992.
M. Khan and H. Al-Jahdali, “The consequences of sleep deprivation on cognitive performance,” King Abdullah International Medical Research Center, vol. 28, no. 2, pp. 91–99, Apr. 2023, doi: https://doi.org/10.17712/nsj.2023.2.20220108.
