Imaginary Control of a Mobile Vehicle Using Deep Learning Algorithm: A Brain Computer Interface Study

Amin Hekmatmanesh,Huapeng Wu,Ming Li,Heikki Handroos,Hamed Mohammadi Azni,Mohsen Afsharchi

doi:10.1109/access.2021.3128611

Amin Hekmatmanesh, Huapeng Wu + Show 4 more

Open Access

https://doi.org/10.1109/access.2021.3128611

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Abstract

Controlling a remote mobile vehicle using electroencephalograph (EEG) signals is still a challenge specially achieving a high degree of accuracy and precision. In the present study, the focus is on implementing an efficient feature space in a deep-based learning (DL) algorithm for a single trial application. More specifically, a boosting feature algorithm by means of long short-term memory (LSTM) networks are implemented in a deep auto-encoder (DAE) algorithm for producing an effective feature space tp identify event related desynchronization/event related synchronization (ERD/ERS) patters in EEG signals. For this purpose, three different DL-based algorithms are implemented that the models are based on a convolutional neural network (CNN), DAE, and LSTM networks to extract and boost the main features. In addition, our previous improved support vector machine (SVM)-based algorithm is employed to consider the potential of SVM and implemented DL-based algorithms for a two classes identification. To consider the efficiency of our implemented methods, algorithms are employed for control of a remote mobile vehicle in an imaginary right-hand opening and making a right-hand fist task. In our experiment, eleven subjects participated in an imaginary movement task. In the experiment, the displayed movement pictures were colored in yellow and red colors for stimulating brain to generate stronger ERD/ERS patterns. Results showed that the proposed algorithm by using the boosting technique significantly increased the accuracy with a higher precision of 73.31% ± 0.03. The proposed method enabled the DL algorithm to be used in single trial experiments.

Highlights

The brain is the main organ that controls the human body and several techniques have been developed to decode the brain neurons activities to explore how the human body controlled
The event related desynchronization/event related synchronization (ERD/ERS) pattern identification results are presented in Table 1, which are based on the accuracy and statistical analysis of the paired t-test statistical and ANOVA values
The results show that the obtained deep auto-encoder (DAE)-long short-term memory (LSTM) accuracy is close to the soft margin support vector machine (SSVM)-generalized radial bases functions (GRBF) accuracy with better precision, which means the DAE-LSTM has the potential of being used in single trial applications if the feature space is well-organized

Summary

Introduction

The brain is the main organ that controls the human body and several techniques have been developed to decode the brain neurons activities to explore how the human body controlled. Brain stroke disability is a terrible situation for people who have had an active role in society. These patients need full-time assistance for their normal daily activities. We focus on the central area which is related to the (imaginary) movement patterns, named event related desynchronization/event related synchronization (ERD/ERS). One important aspect of research focuses on the intention to move (ERD pattern) and the onset of real body movements (ERS pattern). Some of the related known patterns to the movements are error related potential [6], readiness potential [3], ERD/ERS [7], [8], and event related potentials (ERP) patterns such as P300 and steady state visually evoked potential patterns [9]

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