Document Type



Doctor of Philosophy (PhD)


Electrical and Computer Engineering

First Advisor's Name

Ou Bai

First Advisor's Committee Title

Committee chair

Second Advisor's Name

Armando Barreto

Second Advisor's Committee Title

Committee member

Third Advisor's Name

Jean Andrian

Third Advisor's Committee Title

Committee member

Fourth Advisor's Name

Hai Deng

Fourth Advisor's Committee Title

Committee member

Fifth Advisor's Name

Wei-Chiang Lin

Fifth Advisor's Committee Title

Committee member


Brain-computer interface, rehabilitation, volitional control, affective computing, environmental awareness, electroencephalography, biomedical signal processing, machine learning, deep learning, computer vision, neuroengineering.

Date of Defense



Early and reliable prediction of user’s intention to change locomotion mode or speed is critical for a smooth and natural lower limb prosthesis. Meanwhile, incorporation of explicit environmental feedback can facilitate context aware intelligent prosthesis which allows seamless operation in a variety of gait demands. This dissertation introduces environmental awareness through computer vision and enables early and accurate prediction of intention to start, stop or change speeds while walking. Electromyography (EMG), Electroencephalography (EEG), Inertial Measurement Unit (IMU), and Ground Reaction Force (GRF) sensors were used to predict intention to start, stop or increase walking speed. Furthermore, it was investigated whether external emotional music stimuli could enhance the predictive capability of intention prediction methodologies. Application of advanced machine learning and signal processing techniques on pre-movement EEG resulted in an intention prediction system with low latency, high sensitivity and low false positive detection. Affective analysis of EEG suggested that happy music stimuli significantly (p





Previously Published In

Hasan, S.S. and Bai, O., 2021, October. VMD-WSST: A Combined BCI Algorithm to Predict Self-paced Gait Intention. In 2021 IEEE International Conference on Systems, Man, and Cybernetics (SMC) (pp. 3188-3193). IEEE.

Hasan, S.S., Marquez, J.S., Siddiquee, M.R., Fei, D.Y. and Bai, O., 2021. Preliminary Study on Real-time Prediction of Gait Acceleration Intention from VolitionAssociated EEG Patterns. IEEE Access, 9, pp.62676-62686.

Hasan, S.S., Siddiquee, M.R., Atri, R., Ramon, R., Marquez, J.S. and Bai, O., 2020. Prediction of gait intention from pre-movement EEG signals: a feasibility study. Journal of neuroengineering and rehabilitation, 17(1), pp.1-16.

Hasan, S.S., Siddiquee, M.R., Marquez, J.S. and Bai, O., 2020. Enhancement of movement intention detection using EEG signals responsive to emotional music stimulus. IEEE Transactions on Affective Computing.

Hasan, S.S., Siddiquee, M.R. and Bai, O., 2020. Asynchronous prediction of human gait intention in a pseudo online paradigm using wavelet transform. IEEE Transactions on Neural Systems and Rehabilitation Engineering, 28(7), pp.1623-1635.

Hasan, S.S., Siddiquee, M.R. and Bai, O., 2019, December. Supervised classification of EEG signals with score threshold regulation for pseudo-online asynchronous detection of gait intention. In 2019 18th IEEE International Conference On Machine Learning And Applications (ICMLA) (pp. 1476-1479). IEEE.

Files over 15MB may be slow to open. For best results, right-click and select "Save as..."



Rights Statement

Rights Statement

In Copyright. URI:
This Item is protected by copyright and/or related rights. You are free to use this Item in any way that is permitted by the copyright and related rights legislation that applies to your use. For other uses you need to obtain permission from the rights-holder(s).