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54 | Al-Qaysi1, Al-Saegh, Hussein & Ahmed

Wavelet Performance

Value 800
700
600 Morlet BMP

500 Mother wavelet
400
300

200
100

0
Amor

Training time Test time Log loss

Fig. 6: Training time. test time, log loss for the optimal
hybrid model, and different wavelet mother functions.

(a) [10] Z. Al-Qaysi, et al., "Systematic review of training
environments with motor imagery brain–computer
(b) interface: Coherent taxonomy, open issues and
Fig. 5: ROC (a) for left hand and (b) for right hand. recommendation pathway solution," Health and
Technology, vol. 11, pp. 783-801, 2021.

[11] D. Freer and G.-Z. Yang, "Data augmentation for self-
paced motor imagery classification with C-LSTM,"
Journal of neural engineering, vol. 17, p. 016041, 2020.

[12] X. Wang, et al., "A Hybrid Transfer Learning Approach
for Motor Imagery Classification in Brain-Computer
Interface," in 2021 IEEE 3rd Global Conference on Life
Sciences and Technologies (LifeTech), 2021, pp. 496-500.

[13] W. Wei, et al., "A transfer learning framework for
RSVP-based brain computer interface," in 2020 42nd
Annual International Conference of the IEEE Engineering
in Medicine & Biology Society (EMBC), 2020, pp. 2963-
2968.

[14] X. Wei, et al., "Inter-subject deep transfer learning for
motor imagery eeg decoding," in 2021 10th International
IEEE/EMBS Conference on Neural Engineering (NER),
2021, pp. 21-24.

TABLE II
WTNN EVALUATION OVER DATASET-II (TRAINING PART)

Performance Metrics

Subjects Training Testing AUC CA F1 Precision Recall Logloss Specificity
Time Time
S1 16.456 1.00 0.997 0.997 0.997 0.997 0.014 0.997
S2 301.676 24.112 1.00 1.00 1.00 1.00 1.00 0.004 1.00
S3 322.176 16.285 1.00 0.997 0.997 0.997 0.997 0.006 0.997
S4 299.950 17.193 0.994 0.997 0.997 0.997 0.997 0.035 0.997
S5 294.909 17.040 0.996 0.994 0.994 0.994 0.994 0.038 0.994
S6 292.311 15.267 1.00 0.997 0.997 0.997 0.997 0.008 0.997
S7 310.226 16.797 1.00 0.997 0.997 0.997 0.997 0.006 0.997
S8 305.679 17.225 1.00 0.997 0.997 0.997 0.997 0.014 0.997
S9 335.509 16.175 0.994 0.997 0.997 0.997 0.997 0.031 0.997
Mean 293.354 17.39444 0.998222 0.997 0.997 0.997 0.997 0.017333 0.997
306.1989