EEG-based BCI Systems in Neuropsychiatric Diseases

193

[50] Y. Li, L. Guo, Y. Liu, et al., “A temporal-spectral-based squeeze-and-

excitation feature fusion network for motor imagery eeg decoding,” IEEE

Transactions on Neural Systems and Rehabilitation Engineering, vol. 29,

pp. 1534–1545, 2021.

[51] H. Li, M. Ding, R. Zhang, et al., “Motor imagery eeg classification al-

gorithm based on cnn-lstm feature fusion network,” Biomedical Signal

Processing and Control, vol. 72, p. 103342, 02 2022.

[52] V. J. Lawhern, A. J. Solon, N. R. Waytowich, et al., “Eegnet: a compact

convolutional neural network for eeg-based brain–computer interfaces,”

Journal of Neural Engineering, vol. 15, p. 056013, 07 2018.

[53] C. Ieracitano, F. C. Morabito, A. Hussain, et al., “A hybrid-domain deep

learning-based bci for discriminating hand motion planning from eeg

sources,” International Journal of Neural Systems, vol. 31, p. 2150038,

08 2021.

[54] M. Krauledat, M. Tangermann, B. Blankertz, et al., “Towards zero train-

ing for brain-computer interfacing,” PLoS ONE, vol. 3, p. e2967, 08 2008.

[55] H. Kang, Y. Nam, and S. Choi, “Composite common spatial pattern

for subject-to-subject transfer,” IEEE Signal Processing Letters, vol. 16,

p. 683–686, 08 2009.

[56] P. von Bünau, F. C. Meinecke, F. J. Király, et al., “Finding stationary

subspaces in multivariate time series,” Physical Review Letters, vol. 103,

11 2009.

[57] M. E. Özelbaş, E. E. Tülay, and S. Ozekes, “Improving cross-subject clas-

sification performance of motor imagery signals: A data augmentation-

focused deep learning framework,” Machine Learning: Science and Tech-

nology, 01 2024.

[58] R. Mane, T. Chouhan, and C. Guan, “Bci for stroke rehabilitation: motor

and beyond.,” Journal of neural engineering, vol. 17, p. 041001, 2020.

[59] M. J. Young, D. J. Lin, and L. R. Hochberg, “Brain–computer inter-

faces in neurorecovery and neurorehabilitation,” Seminars in Neurology,

vol. 41, pp. 206–216, 03 2021.

[60] R. Katmah, F. Al-Shargie, U. Tariq, et al., “A review on mental stress

assessment methods using eeg signals,” Sensors, vol. 21, p. 5043, 07 2021.

[61] 2021 9th International Winter Conference on Brain-Computer Interface

(BCI), Evaluation and Diagnosis of Brain Diseases based on Non-invasive

BCI, IEEE, 02 2021.