Small-Dimension Feature Matrix Construction Method for Decoding Repetitive Finger Movements From Electroencephalogram Signals

Jia, Tianyu; Liu, Ke; Lu, Yijia; Liu, Yali; Liu, Chong; Ji, Linhong; Qian, Chao

doi:10.1109/access.2020.2982210

Cited by 5 publications

(6 citation statements)

References 45 publications

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“…For example, in [29], average classification accuracy is achieved through the use of different feature extraction and classification algorithms, which are known to increase runtime. Moreover, contrary to other methods presented in [29], [36]- [38] the proposed approach improves the runtime by reducing the number of studied samples and channels while ensuring a high accuracy. In fact, the proposed approach allows to localize the EEG epoch while optimizing the length of the frame window as much as possible instead of using a fixing starting time point of MI and a fixing epoch duration as in the case of the study presented in [29]; which has used a fixed frame window of 2s.…”

Section: Resultsmentioning

confidence: 89%

An Effective Zeros-Time Windowing Strategy to Detect Sensorimotor Rhythms Related to Motor Imagery EEG Signals

2020

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Brain-computer interface (BCI) acquires, analyzes and transforms human brain activity to control commands allowing as such disabled people to communicate or control external devices. A motor imagery-based BCI enables patients to control artificial peripherals and communicate with the outside world by merely thinking of the task such as, e.g., the imagination of left-hand, right-hand, or foot movement. The mere intention of moving one of the limbs triggers neural activity, which is induced in the primary sensorimotor areas like that observed with real executed movements. Tracking generated sensorimotor rhythms (SMRs) and extracting robust and informative features from electroencephalogram (EEG) signals are challenging due to the time-varying nature of EEG signals and the inter-human variability. In this paper, we proposed an EEG-zeros-time windowing (E2ZTW) approach based on a highly decaying window function to track SMRs and identify the temporal epochs containing useful information without any prior information on the trigger. The proposed approach involves the application of the group-delay function, allowing the improvement of the spectral resolution due to the additive property of the function on individual resonances. Some algorithms were integrated into the proposed approach, such as the common spatial pattern algorithm, which is used to extract features and linear discriminant analysis and a convolutional neural network, which are used for the classification of the features. The effectiveness of the proposed approach in tracking the SMRs rhythms is evaluated in terms of accuracy. Experiments were performed on three public datasets provided by BCI competition for 17 subjects. Following experimental results, it is shown that discrimination between the left-and right-hand movements can be achieved within a few seconds with high classification accuracy. As compared to other state-of-art techniques, the proposed approach achieves an average classification accuracy and standard error values of 82% and 13, respectively, thereby outperforming existing algorithm by an accuracy mean of 2%.

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Section: Resultsmentioning

confidence: 89%

An Effective Zeros-Time Windowing Strategy to Detect Sensorimotor Rhythms Related to Motor Imagery EEG Signals

2020

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“…The proposed finger decoding system outperforms those of the previous studies, e.g., the average accuracy herein increased by 4% compared to the best previous system presented in [14]. Moreover, the proposed system significantly improves the runtime using a robust and efficient algorithms, contrary to the method presented in [14,11,12,22]. classifier was trained on a portion of the data set and tested using another portion.…”

Section: Discussionmentioning

confidence: 86%

EEG-Based BCI System to Control Prosthesis's Finger Movements

Gannouni¹,

Belwafi²,

Aboalsamh³

et al. 2020

Preprint

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Background: The advances in assistive technologies will go a long way towards restoring the mobility of paralyzed and/or amputated limbs. In this paper, we propose a system that adopts the brain-computer interface ( BCI ) technology to control prosthetic fingers by thoughts. To predict the movements of each finger, a complex EEG signal processing algorithms should be applied in order to remove the outliers , to extract feature, to discriminate between the fingers and to control prosthesis's finger. The proposed method discriminates between the five human fingers. So a multi -classification problem based on ensemble of one class-classifier is applied where each classifier predicts the intention to move one finger. At the end, an adapted machine learning strategy is proposed to predict movements of multiple fingers at the same time. Results: The sensitive regions of the brain related to finger movements are identified and located. The proposed EEG signal processing chain, based on ensemble of one class-classifier, reach a classification accuracy of 81 \ % for five subjects according to the online approach. Unlike most of the existing prototypes that allow to control only one single finger and to perform only one movement at a time by the dedicated finger, our proposed system will enable multiple fingers to perform movements simultaneously. Despite that the proposed system classifies a five tasks, the obtained accuracy is too high compared to a binary classification system. Conclusion: The proposed system contributes to the advancement of a prosthetic allowing people with severe disabilities to do the daily tasks easily.

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“…Different types of bio-signals such as EEG [ 7 , 8 , 9 , 10 ], Magnetoencephalography (MEG) [ 11 , 12 ], Electrocorticogram (ECoG) [ 13 , 14 , 15 ], Functional Near-Infrared Spectroscopy (fNIRS) [ 16 ], as well as Electromyography (EMG) [ 17 ] have been used to design BCI systems that decode finger movements. EEG is the most commonly used technology for the acquisition of brain signals in BCI systems due to its noninvasive nature, low cost, and high portability [ 8 ].…”

Section: Introductionmentioning

confidence: 99%

“…Ref. [ 7 ] analyzed Event-related desynchronization (ERD) topography during left or right index finger movement. A degree feature extraction algorithm was proposed based on the graph theory together with Support Vector Machine (SVM) to classify two kinds of index finger movement: left or right index finger movement.…”

Section: Introductionmentioning

confidence: 99%

“…Previous EEG-based studies [ 7 , 8 , 9 , 10 ] were usually extracting features from the same group of electrodes for all subjects and for all fingers movements, whereas neuroscience studies mentioned that the activity is unique in the brain of each person. Moreover, the brain activity is different in the left and right brain hemispheres during the same motor imagery [ 7 , 21 ]. Thus, there is a growing need to analyze the brain activity during finger movements and to identify the electrodes that are showing relevant and significant changes.…”

Section: Introductionmentioning

confidence: 99%

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EEG-Based BCI System to Detect Fingers Movements

et al. 2020

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The advancement of assistive technologies toward the restoration of the mobility of paralyzed and/or amputated limbs will go a long way. Herein, we propose a system that adopts the brain-computer interface technology to control prosthetic fingers with the use of brain signals. To predict the movements of each finger, complex electroencephalogram (EEG) signal processing algorithms should be applied to remove the outliers, extract features, and be able to handle separately the five human fingers. The proposed method deals with a multi-class classification problem. Our machine learning strategy to solve this problem is built on an ensemble of one-class classifiers, each of which is dedicated to the prediction of the intention to move a specific finger. Regions of the brain that are sensitive to the movements of the fingers are identified and located. The average accuracy of the proposed EEG signal processing chain reached 81% for five subjects. Unlike the majority of existing prototypes that allow only one single finger to be controlled and only one movement to be performed at a time, the system proposed will enable multiple fingers to perform movements simultaneously. Although the proposed system classifies five tasks, the obtained accuracy is too high compared with a binary classification system. The proposed system contributes to the advancement of a novel prosthetic solution that allows people with severe disabilities to perform daily tasks in an easy manner.

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Small-Dimension Feature Matrix Construction Method for Decoding Repetitive Finger Movements From Electroencephalogram Signals

Cited by 5 publications

References 45 publications

An Effective Zeros-Time Windowing Strategy to Detect Sensorimotor Rhythms Related to Motor Imagery EEG Signals

An Effective Zeros-Time Windowing Strategy to Detect Sensorimotor Rhythms Related to Motor Imagery EEG Signals

EEG-Based BCI System to Control Prosthesis's Finger Movements

EEG-Based BCI System to Detect Fingers Movements

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