Preliminary Study on Real-Time Prediction of Gait Acceleration Intention From Volition-Associated EEG Patterns

• Main Authors: S. M. Shafiul Hasan, J. Sebastian Marquez, Masudur R. Siddiquee, Ding-Yu Fei, Ou Bai
• Format: Article
• Language: English
• Published: IEEE 2021-01-01
• Series: IEEE Access
• Subjects: Brain-computer interface; gait acceleration intention; threshold regulation; majority voting; wavelet transform; event related desynchronization
• Online Access: https://ieeexplore.ieee.org/document/9411857/

Volitional control of prosthetic devices can potentially provide the user with a more natural movement experience. Currently, there is no feasible volitional triggering method to adapt the prosthetic device to user’s intention to accelerate during walking. Therefore, real-time prediction of human acceleration intention from the pre-acceleration electroencephalogram (EEG), and subsequent adaptation of the prosthetic device’s control parameters for seamless transition remains a daunting research area. In that aspect, this study investigates the neural changes responsive to human intention to accelerate during walking. Furthermore, this study also explores whether the acceleration intention can be predicted from real-time EEG to subsequently enable parametric adaptation for an external prosthetic device. EEG, Inertial Measurement Unit (IMU), and ground reaction force (GRF) signals were collected from one healthy subject during walking with self-paced speed changes. A set of classifiers were explored to classify between constant speed and acceleration. The classifiers showed promising classification performance well above chance level in offline, pseudo-online and real-time scenarios. An accuracy of 85.9±2.9% was achieved in offline scenario, while pseudo-online classification resulted in a true positive rate (TPR) of 81.9±7.4% with 7.7±0.8 false positives/ min and a detection latency of −844±572 ms. In real-time scenario, 9 out of 12 acceleration events were predicted successfully with only 3 false predictions at an average latency of −741 ms. Moreover, offline data analysis suggested suppression of mu and beta rhythms related to gait acceleration between 2 seconds before and 1.5 seconds after the onset of acceleration. A slow increase in negative amplitude was also observed in near DC frequencies of EEG data acquired from the sensorimotor cortex. The findings of this study portray inspiring results in retrieving gait acceleration-associated neural changes for facilitating natural control of assistive devices.