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Restoring After Central Nervous system Injuries:Neural Mechanisms and Translational Applications of Motor Recovery

Neuroscience Bulletin 2022年第12期38卷 1569-1587页

作者： Zhengrun Gao Zhen Pang Yiming Chen Gaowei Lei Shuai Zhu Guotao Li Yundong Shen Wendong Xu Department of Hand Surgery Huashan HospitalFudan UniversityShanghai 200040China The National Clinical Research Center for Aging and Medicine Fudan UniversityShanghai 200040China Department of Hand and Upper Extremity Surgery Jing’an District Central HospitalFudan UniversityShanghai 200040China Institutes of Brain Science Fudan UniversityShanghai 200040China State Key Laboratory of Medical Neurobiology and MOE Frontiers Center for Brain Science Fudan UniversityShanghai 200040China Institute of Automation State Key Laboratory of Management and Control for Complex SystemsChinese Academy of SciencesBeijing 100190China Co-innovation Center of Neuroregeneration Nantong UniversityNantong 226000China Research Unit of Synergistic Reconstruction of Upper and Lower Limbs after Brain Injury Chinese Academy of Medical SciencesShanghai 200400China

Central nervous system(CNS)injuries,including stroke,traumatic brain injury,and spinal cord injury,are leading causes of long-term *** is estimated that more than half of the survivors of severe unilateral injury are unable to use the denervated *** studies have focused on neuroprotective interventions in the affected hemisphere to limit brain lesions and neurorepair measures to promote ***,the ability to increase plasticity in the injured brain is restricted and difficult to ***,over several decades,researchers have been prompted to enhance the compensation by the unaffected *** experiments have revealed that regrowth of ipsilateral descending fibers from the unaffected hemisphere to denervated motor neurons plays a significant role in the restoration of motor *** addition,several clinical treatments have been designed to restore ipsilateral motor control,including brain stimulation,nerve transfer surgery,and brain–computer interface ***,we comprehensively review the neural mechanisms as well as translational applications of ipsilateral motor control upon rehabilitation after CNS injuries.

关键词： Stroke Traumatic brain injury Spinal cord injury brain-computer interface system Neuroplasticity Ipsilateral motor control Axon regrowth

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Protection of BCI system via reversible watermarking of EEG signal

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ELECTRONICS LETTERS 2020年第25期56卷 1389-U30页

作者： Bhalerao, S. Ansari, I. A. Kumar, A. PDPM Indian Inst Informat Technol Design & Mfg Elect & Commun Engn Jabalpur 482005 MP India

The brain-computer interface (BCI) system is doing wonders for people suffering from restricted physical abilities due to accidents or diseases. BCI requires recording of electroencephalogram (EEG) from the subject in order to control electrical devices. In the wireless BCI system, there is always a possibility of EEG signal tampering/attacking during transmission, which may result in malfunction of the BCI system. This work introduces a security block in the BCI system that can ensure that the recorded EEG signals are intact at the receiving end of wireless BCI system. The security block identifies any tampering as well as authenticates the EEG signals at receiving end. Any change in EEG signal can result into classification error;because of that prediction error expansion based reversible watermarking approach has been used. The proposed scheme works efficiently and performance of BCI system remains unaltered after inclusion of security block.

关键词： medical signal processing image watermarking data encapsulation diseases electroencephalography watermarking brain-computer interfaces EEG signal security block brain-computer interface system wireless BCI system recorded EEG signals

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Convolutional Neural Network Based Approach Towards Motor Imagery Tasks EEG Signals Classification

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IEEE SENSORS JOURNAL 2019年第12期19卷 4494-4500页

作者： Chaudhary, Shalu Taran, Sachin Bajaj, Varun Sengur, Abdulkadir Indian Inst Informat Technol Design & Mfg Elect & Commun Dept Jabalpur 482005 India Firat Univ Fac Technol Elect & Elect Engn Dept TR-23119 Elazig Turkey

This paper introduces a methodology based on deep convolutional neural networks (DCNN) for motor imagery (MI) tasks recognition in the brain-computer interface (BCI) system. More specifically, the DCNN is used for classification of the right hand and right foot MI-tasks based electroencephalogram (EEG) signals. The proposed method first transforms the input EEG signals into images by applying the time-frequency (T-F) approaches. The used T-F approaches are short-time-Fourier-transform (STFT) and continuous-wavelet-transform (CWT). After T-F transformation the images of MI-tasks EEG signals are applied to the DCNN stage. The pre-trained DCNN model, AlexNet is explored for classification. The efficiency of the proposed method is evaluated on IVa dataset of BCI competition-III. The evaluation metrics such as accuracy, sensitivity, specificity, F1-score, and kappa value are used for measuring the proposed method results quantitatively. The obtained results show that the CWT approach yields better results than the STFT approach. In addition, the proposed method obtained 99.35% accuracy score is the best one among the existing methods accuracy scores.

关键词： Electroencephalogram (EEG) signal brain-computer interface system motor imagery short time Fourier transform continuous wavelet transform convolutional neural network

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Motor imagery tasks-based EEG signals classification using tunable-Q wavelet transform

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NEURAL COMPUTING & APPLICATIONS 2019年第11期31卷 6925-6932页

作者： Taran, Sachin Bajaj, Varun PDPM Indian Inst Informat Technol Design & Mfg Ja Discipline Elect & Commun Engn Jabalpur 452005 India

Motor imagery (MI) tasks-based brain-computer interface (BCI) system finds applications for disabled people to communicate with surrounding. The BCI system reliability is relied on how well the different MI tasks are assessed and identified. Electroencephalogram (EEG) recordings provide a noninvasive way for imaging of MI tasks in BCI system. In this framework, tunable-Q wavelet transform (TQWT)-based feature extraction method is proposed for the classification of different MI tasks EEG signals. The TQWT parameters are tuned for the decomposition of EEG signal into sub-bands. Time domain measures of sub-bands are considered as features for MI tasks EEG signals. The TQWT-based features are tested on least-squares support vector machine classifier for the classification of right-hand and right-foot MI tasks. The proposed method provides 96.89% MI tasks classification accuracy, which is the highest as compared to other existing same data set methods. The suggested method can be used for identification of MI tasks in a BCI system designed for controlling robotic arm and wheel chairs, etc.

关键词： Electroencephalogram (EEG) signal brain-computer interface system Tunable-Q wavelet transform Least-squares support vector machine

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Mind your thoughts: BCI using single EEG electrode

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IET CYBER-PHYSICAL systemS: THEORY & APPLICATIONS 2019年第2期4卷 164-172页

作者： Narayana, Sujay Prasad, RangaRao Venkatesha Warmerdam, Kevin Delft Univ Technol Embedded Software Dept Delft Netherlands

These days, the Internet of things (IoT) research is driving large-scale development and deployment of many innovative applications. IoT has indeed brought many smart applications to the doorstep of users. IoT has also made it possible to connect many sensors and control equipment. Here, the authors address an important application for physically challenged. The authors present a brain-computer interface (BCI) system to lock/unlock a wheelchair and control its movements using BCI. The approach presented here uses NeuroSky's MindWave Mobile, a single electrode electroencephalography (EEG) headset that can be connected to any Bluetooth-enabled system. The raw EEG data from the headset is processed on an Android mobile device to extract the electromyography (EMG) patterns that occur due to eye blinks and activity of muscles in the jaw. These patterns are used to control the movement of a wheelchair in all possible directions. A biometric security system is provided to lock and unlock the wheelchair by extracting the information about different brain waves from the raw EEG signal. In this system, only the user knows the password which is generated using brain waves and it can lock/unlock the wheelchair and control it. The proposed system was verified and evaluated using a prototype.

关键词： Bluetooth mobile computing handicapped aids biometrics (access control) electroencephalography brain-computer interfaces medical signal processing control engineering computing biomedical electrodes Internet of Things smart applications IoT sensors control equipment brain-computer interface system BCI wheelchair single electrode electroencephalography headset raw EEG data Android mobile device electroencephalography patterns biometric security system raw EEG signal large-scale development innovative applications brain waves EEG electrode Internet of Things research NeuroSky MindWave Mobile

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A flexible analytic wavelet transform based approach for motor-imagery tasks classification in BCI applications

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computer METHODS AND PROGRAMS IN BIOMEDICINE 2020年第0期187卷 105325-000页

作者： Chaudhary, Shalu Taran, Sachin Bajaj, Varun Siuly, Siuly PDPM Indian Inst Informat Technol Design & Mfg Discipline Elect & Commun Engn Jabalpur 452005 India Victoria Univ Inst Sustainable Ind & Liveable Cities Melbourne Vic Australia

Background and Objective: Motor Imagery (MI) based brain-computer-interface (BCI) is a rising support system that can assist disabled people to communicate with the real world, without any external help. It serves as an alternative communication channel between the user and computer. Electroencephalogram (EEG) recordings prove to be an appropriate choice for imaging MI tasks in a BCI system as it provides a non-invasive way for completing the task. The reliability of a BCI system confides on the efficiency of the assessment of different MI tasks. Methods: The present work proposes a new approach for the classification of distinct MI tasks based on EEG signals using the flexible analytic wavelet transform (FAWT) technique. The FAWT decomposes the EEG signal into sub-bands and temporal moment-based features are extracted from the sub-bands. Feature normalization is applied to minimize the bias nature of classifier. The FAWT-based features are utilized as inputs to multiple classifiers. Ensemble learning method based Subspace k-Nearest Neighbour (kNN) classifier is established as the best and robust classifier for the distinction of the right hand (RH) and right foot (RF) MI tasks. Results: The sub-band (SB) wise features are tested on multiple classifiers and best performance parameters are obtained using the ensemble method based subspace kNN classifier. The best results of parameters are obtained for fourth SB as accuracy 99.33%, sensitivity 99%, specificity 99.6%, Fl-Score 0.9925, and kappa value 0.9865. The other sub-bands are also attained significant results using subspace KNN classifier. Conclusions: The proposed work explores the utility of FAWT based features for the classification of RH and RF MI tasks EEG signals. The suggested work highlights the effectiveness of multiple classifiers for classification MI-tasks. The proposed method shows better performance in comparison to state-of-arts methods. Thus, the potential to implement a BCI system for controlling

关键词： Electroencephalogram(EEG) signal brain-computer interface system Motor imagery Flexible analytic wavelet transform Ensemble methods

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Permutation Entropy: a new feature for brain-computer interfaces

Permutation Entropy: a new feature for Brain-Computer Interf...

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Biomedical Circuits and systems Conference (BioCAS)

作者： Nicolaou, Nicoletta Georgiou, Julius Univ Cyprus Dept Elect & Comp Engn KIOS Holist Elect Res Lab Kallipoleos 75 CY-1678 Nicosia Cyprus

ISBN: (纸本)9781424472703

This paper investigates the use of Permutation Entropy (PE) as a feature for mental task classification for a brain-computer interface system. PE is a recently introduced measure which quantifies signal complexity by measuring the departure of a time series from a random one. More regular signals are characterized by lower PE values. Here, PE is utilized to characterize signals from electroencephalograms of 3 subjects performing 4 motor imagery tasks, which are then classified using a Support Vector Machine. Even though it is possible to obtain 100% single-trial classification accuracy, this is very much subject-dependent.

关键词： brain-computer interface system brain-computer interfaces electroencephalograms electroencephalography entropy medical signal processing mental task classification motor imagery pattern classification permutation entropy support vector machine support vec

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