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Mridha MF, Das SC, Kabir MM, Lima AA, Islam MR, Watanobe Y. Brain-Computer Interface: Advancement and Challenges. SENSORS 2021; 21:s21175746. [PMID: 34502636 PMCID: PMC8433803 DOI: 10.3390/s21175746] [Citation(s) in RCA: 13] [Impact Index Per Article: 4.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 07/17/2021] [Revised: 08/15/2021] [Accepted: 08/20/2021] [Indexed: 02/04/2023]
Abstract
Brain-Computer Interface (BCI) is an advanced and multidisciplinary active research domain based on neuroscience, signal processing, biomedical sensors, hardware, etc. Since the last decades, several groundbreaking research has been conducted in this domain. Still, no comprehensive review that covers the BCI domain completely has been conducted yet. Hence, a comprehensive overview of the BCI domain is presented in this study. This study covers several applications of BCI and upholds the significance of this domain. Then, each element of BCI systems, including techniques, datasets, feature extraction methods, evaluation measurement matrices, existing BCI algorithms, and classifiers, are explained concisely. In addition, a brief overview of the technologies or hardware, mostly sensors used in BCI, is appended. Finally, the paper investigates several unsolved challenges of the BCI and explains them with possible solutions.
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Affiliation(s)
- M. F. Mridha
- Department of Computer Science and Engineering, Bangladesh University of Business and Technology, Dhaka 1216, Bangladesh; (M.F.M.); (S.C.D.); (M.M.K.); (A.A.L.)
| | - Sujoy Chandra Das
- Department of Computer Science and Engineering, Bangladesh University of Business and Technology, Dhaka 1216, Bangladesh; (M.F.M.); (S.C.D.); (M.M.K.); (A.A.L.)
| | - Muhammad Mohsin Kabir
- Department of Computer Science and Engineering, Bangladesh University of Business and Technology, Dhaka 1216, Bangladesh; (M.F.M.); (S.C.D.); (M.M.K.); (A.A.L.)
| | - Aklima Akter Lima
- Department of Computer Science and Engineering, Bangladesh University of Business and Technology, Dhaka 1216, Bangladesh; (M.F.M.); (S.C.D.); (M.M.K.); (A.A.L.)
| | - Md. Rashedul Islam
- Department of Computer Science and Engineering, University of Asia Pacific, Dhaka 1216, Bangladesh
- Correspondence:
| | - Yutaka Watanobe
- Department of Computer Science and Engineering, University of Aizu, Aizu-Wakamatsu 965-8580, Japan;
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Larzabal C, Auboiroux V, Karakas S, Charvet G, Benabid AL, Chabardès S, Costecalde T, Bonnet S. The Riemannian Spatial Pattern method: mapping and clustering movement imagery using Riemannian geometry. J Neural Eng 2021; 18. [PMID: 33770779 DOI: 10.1088/1741-2552/abf291] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/14/2020] [Accepted: 03/26/2021] [Indexed: 11/11/2022]
Abstract
OBJECTIVE Over the last decade, Riemannian geometry has shown promising results for motor imagery classification. However, extracting the underlying spatial features is not as straightforward as for applying Common Spatial Pattern (CSP) filtering prior to classification. In this article, we propose a simple way to extract the spatial patterns obtained from Riemannian classification: the Riemannian Spatial Pattern (RSP) method, which is based on the backward channel selection procedure. APPROACH The RSP method was compared to the CSP approach on ECoG data obtained from a quadriplegic patient while performing imagined movements of arm articulations and fingers. MAIN RESULTS Similar results were found between the RSP and CSP methods for mapping each motor imagery task with activations following the classical somatotopic organization. Clustering obtained by pairwise comparisons of imagined motor movements however, revealed higher differentiation for the RSP method compared to the CSP approach. Importantly, the RSP approach could provide a precise comparison of the imagined finger flexions which added supplementary information to the mapping results. SIGNIFICANCE Our new RSP method illustrates the interest of the Riemannian framework in the spatial domain and as such offers new avenues for the neuroimaging community. This study is part of an ongoing clinical trial registered with ClinicalTrials.gov, NCT02550522.
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Affiliation(s)
| | - Vincent Auboiroux
- CEA de Grenoble, Clinatec, Grenoble, Auvergne-Rhône-Alpes, 38054, FRANCE
| | - Serpil Karakas
- CEA de Grenoble, Clinatec, Grenoble, Auvergne-Rhône-Alpes, 38054, FRANCE
| | - Guillaume Charvet
- CEA de Grenoble, Clinatec, Grenoble, Auvergne-Rhône-Alpes, 38054, FRANCE
| | - Alim-Louis Benabid
- CEA de Grenoble, Clinatec, Grenoble, Auvergne-Rhône-Alpes, 38054, FRANCE
| | | | - Thomas Costecalde
- CEA de Grenoble, Clinatec, Grenoble, Auvergne-Rhône-Alpes, 38054, FRANCE
| | - Stephane Bonnet
- CEA de Grenoble, DTBS, Grenoble, Auvergne-Rhône-Alpes, 38000, FRANCE
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Spatial-Frequency Feature Learning and Classification of Motor Imagery EEG Based on Deep Convolution Neural Network. COMPUTATIONAL AND MATHEMATICAL METHODS IN MEDICINE 2020; 2020:1981728. [PMID: 32765639 PMCID: PMC7387988 DOI: 10.1155/2020/1981728] [Citation(s) in RCA: 14] [Impact Index Per Article: 3.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 11/26/2019] [Revised: 01/30/2020] [Accepted: 02/20/2020] [Indexed: 11/19/2022]
Abstract
EEG pattern recognition is an important part of motor imagery- (MI-) based brain computer interface (BCI) system. Traditional EEG pattern recognition algorithm usually includes two steps, namely, feature extraction and feature classification. In feature extraction, common spatial pattern (CSP) is one of the most frequently used algorithms. However, in order to extract the optimal CSP features, prior knowledge and complex parameter adjustment are often required. Convolutional neural network (CNN) is one of the most popular deep learning models at present. Within CNN, feature learning and pattern classification are carried out simultaneously during the procedure of iterative updating of network parameters; thus, it can remove the complicated manual feature engineering. In this paper, we propose a novel deep learning methodology which can be used for spatial-frequency feature learning and classification of motor imagery EEG. Specifically, a multilayer CNN model is designed according to the spatial-frequency characteristics of MI EEG signals. An experimental study is carried out on two MI EEG datasets (BCI competition III dataset IVa and a self-collected right index finger MI dataset) to validate the effectiveness of our algorithm in comparison with several closely related competing methods. Superior classification performance indicates that our proposed method is a promising pattern recognition algorithm for MI-based BCI system.
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Miao M, Zeng H, Wang A, Zhao F, Liu F. Index finger motor imagery EEG pattern recognition in BCI applications using dictionary cleaned sparse representation-based classification for healthy people. THE REVIEW OF SCIENTIFIC INSTRUMENTS 2017; 88:094305. [PMID: 28964180 DOI: 10.1063/1.5001896] [Citation(s) in RCA: 5] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Received: 05/09/2017] [Accepted: 08/26/2017] [Indexed: 06/07/2023]
Abstract
Electroencephalogram (EEG)-based motor imagery (MI) brain-computer interface (BCI) has shown its effectiveness for the control of rehabilitation devices designed for large body parts of the patients with neurologic impairments. In order to validate the feasibility of using EEG to decode the MI of a single index finger and constructing a BCI-enhanced finger rehabilitation system, we collected EEG data during right hand index finger MI and rest state for five healthy subjects and proposed a pattern recognition approach for classifying these two mental states. First, Fisher's linear discriminant criteria and power spectral density analysis were used to analyze the event-related desynchronization patterns. Second, both band power and approximate entropy were extracted as features. Third, aiming to eliminate the abnormal samples in the dictionary and improve the classification performance of the conventional sparse representation-based classification (SRC) method, we proposed a novel dictionary cleaned sparse representation-based classification (DCSRC) method for final classification. The experimental results show that the proposed DCSRC method gives better classification accuracies than SRC and an average classification accuracy of 81.32% is obtained for five subjects. Thus, it is demonstrated that single right hand index finger MI can be decoded from the sensorimotor rhythms, and the feature patterns of index finger MI and rest state can be well recognized for robotic exoskeleton initiation.
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Affiliation(s)
- Minmin Miao
- School of Instrument Science and Engineering, Southeast University, No. 2 Sipailou, Nanjing 210096, China
| | - Hong Zeng
- School of Instrument Science and Engineering, Southeast University, No. 2 Sipailou, Nanjing 210096, China
| | - Aimin Wang
- School of Instrument Science and Engineering, Southeast University, No. 2 Sipailou, Nanjing 210096, China
| | - Fengkui Zhao
- College of Automobile and Traffic Engineering, Nanjing Forest University, No. 159 Longpan Road, Nanjing 210037, China
| | - Feixiang Liu
- School of Instrument Science and Engineering, Southeast University, No. 2 Sipailou, Nanjing 210096, China
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Quitadamo LR, Cavrini F, Sbernini L, Riillo F, Bianchi L, Seri S, Saggio G. Support vector machines to detect physiological patterns for EEG and EMG-based human-computer interaction: a review. J Neural Eng 2017; 14:011001. [PMID: 28068295 DOI: 10.1088/1741-2552/14/1/011001] [Citation(s) in RCA: 47] [Impact Index Per Article: 6.7] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/12/2022]
Abstract
Support vector machines (SVMs) are widely used classifiers for detecting physiological patterns in human-computer interaction (HCI). Their success is due to their versatility, robustness and large availability of free dedicated toolboxes. Frequently in the literature, insufficient details about the SVM implementation and/or parameters selection are reported, making it impossible to reproduce study analysis and results. In order to perform an optimized classification and report a proper description of the results, it is necessary to have a comprehensive critical overview of the applications of SVM. The aim of this paper is to provide a review of the usage of SVM in the determination of brain and muscle patterns for HCI, by focusing on electroencephalography (EEG) and electromyography (EMG) techniques. In particular, an overview of the basic principles of SVM theory is outlined, together with a description of several relevant literature implementations. Furthermore, details concerning reviewed papers are listed in tables and statistics of SVM use in the literature are presented. Suitability of SVM for HCI is discussed and critical comparisons with other classifiers are reported.
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Affiliation(s)
- L R Quitadamo
- Department of Electronic Engineering, University of Rome Tor Vergata, Rome, Italy. School of Life and Health Sciences, Aston Brain Center, Aston University, Birmingham, UK
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Stankevich LA, Sonkin KM, Shemyakina NV, Nagornova ZV, Khomenko JG, Perets DS, Koval AV. EEG pattern decoding of rhythmic individual finger imaginary movements of one hand. ACTA ACUST UNITED AC 2016. [DOI: 10.1134/s0362119716010175] [Citation(s) in RCA: 10] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/23/2022]
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