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Zhang Y, Qiu S, He H. Multimodal motor imagery decoding method based on temporal spatial feature alignment and fusion. J Neural Eng 2023; 20. [PMID: 36854181 DOI: 10.1088/1741-2552/acbfdf] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/12/2022] [Accepted: 02/28/2023] [Indexed: 03/02/2023]
Abstract
Objective. A motor imagery-based brain-computer interface (MI-BCI) translates spontaneous movement intention from the brain to outside devices. Multimodal MI-BCI that uses multiple neural signals contains rich common and complementary information and is promising for enhancing the decoding accuracy of MI-BCI. However, the heterogeneity of different modalities makes the multimodal decoding task difficult. How to effectively utilize multimodal information remains to be further studied.Approach. In this study, a multimodal MI decoding neural network was proposed. Spatial feature alignment losses were designed to enhance the feature representations extracted from the heterogeneous data and guide the fusion of features from different modalities. An attention-based modality fusion module was built to align and fuse the features in the temporal dimension. To evaluate the proposed decoding method, a five-class MI electroencephalography (EEG) and functional near infrared spectroscopy (fNIRS) dataset were constructed.Main results and significance. The comparison experimental results showed that the proposed decoding method achieved higher decoding accuracy than the compared methods on both the self-collected dataset and a public dataset. The ablation results verified the effectiveness of each part of the proposed method. Feature distribution visualization results showed that the proposed losses enhance the feature representation of EEG and fNIRS modalities. The proposed method based on EEG and fNIRS modalities has significant potential for improving decoding performance of MI tasks.
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Affiliation(s)
- Yukun Zhang
- School of Artificial Intelligence, University of Chinese Academy of Sciences, Beijing, People's Republic of China.,Laboratory of Brain Atlas and Brain-Inspired Intelligence, State Key Laboratory of Multimodal Artificial Intelligence Systems, Institute of Automation, Chinese Academy of Sciences, Beijing, People's Republic of China
| | - Shuang Qiu
- School of Artificial Intelligence, University of Chinese Academy of Sciences, Beijing, People's Republic of China.,Laboratory of Brain Atlas and Brain-Inspired Intelligence, State Key Laboratory of Multimodal Artificial Intelligence Systems, Institute of Automation, Chinese Academy of Sciences, Beijing, People's Republic of China
| | - Huiguang He
- School of Artificial Intelligence, University of Chinese Academy of Sciences, Beijing, People's Republic of China.,Laboratory of Brain Atlas and Brain-Inspired Intelligence, State Key Laboratory of Multimodal Artificial Intelligence Systems, Institute of Automation, Chinese Academy of Sciences, Beijing, People's Republic of China
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2
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Apicella A, Arpaia P, Frosolone M, Improta G, Moccaldi N, Pollastro A. EEG-based measurement system for monitoring student engagement in learning 4.0. Sci Rep 2022; 12:5857. [PMID: 35393470 PMCID: PMC8987513 DOI: 10.1038/s41598-022-09578-y] [Citation(s) in RCA: 10] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/28/2021] [Accepted: 03/11/2022] [Indexed: 11/09/2022] Open
Abstract
A wearable system for the personalized EEG-based detection of engagement in learning 4.0 is proposed. In particular, the effectiveness of the proposed solution is assessed by means of the classification accuracy in predicting engagement. The system can be used to make an automated teaching platform adaptable to the user, by managing eventual drops in the cognitive and emotional engagement. The effectiveness of the learning process mainly depends on the engagement level of the learner. In case of distraction, lack of interest or superficial participation, the teaching strategy could be personalized by an automatic modulation of contents and communication strategies. The system is validated by an experimental case study on twenty-one students. The experimental task was to learn how a specific human-machine interface works. Both the cognitive and motor skills of participants were involved. De facto standard stimuli, namely (1) cognitive task (Continuous Performance Test), (2) music background (Music Emotion Recognition-MER database), and (3) social feedback (Hermans and De Houwer database), were employed to guarantee a metrologically founded reference. In within-subject approach, the proposed signal processing pipeline (Filter bank, Common Spatial Pattern, and Support Vector Machine), reaches almost 77% average accuracy, in detecting both cognitive and emotional engagement.
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Affiliation(s)
- Andrea Apicella
- Department of Electrical Engineering and Information Technology, University of Napoli Federico II, Naples, Italy
| | - Pasquale Arpaia
- Department of Electrical Engineering and Information Technology, University of Napoli Federico II, Naples, Italy.
| | - Mirco Frosolone
- Department of Electrical Engineering and Information Technology, University of Napoli Federico II, Naples, Italy.,Department of Public Health and Preventive Medicine, University of Naples Federico II, Naples, Italy
| | - Giovanni Improta
- Department of Public Health and Preventive Medicine, University of Naples Federico II, Naples, Italy
| | - Nicola Moccaldi
- Department of Electrical Engineering and Information Technology, University of Napoli Federico II, Naples, Italy
| | - Andrea Pollastro
- Department of Electrical Engineering and Information Technology, University of Napoli Federico II, Naples, Italy
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Maghsoudi A, Shalbaf A. Hand Motor Imagery Classification Using Effective Connectivity and Hierarchical Machine Learning in EEG Signals. J Biomed Phys Eng 2022; 12:161-170. [PMID: 35433527 PMCID: PMC8995751 DOI: 10.31661/jbpe.v0i0.1264] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/23/2019] [Accepted: 11/14/2019] [Indexed: 11/16/2022]
Abstract
Background Motor Imagery (MI) Brain Computer Interface (BCI) directly links central nervous system to a computer or a device. Most MI-BCI structures rely on features of a single channel of Electroencephalogram (EEG). However, to provide more valuable features, the relationships among EEG channels in the form of effective brain connectivity analysis must be considered. Objective This study aims to identify a set of robust and discriminative effective connectivity features from EEG signals and to develop a hierarchical machine learning structure for discrimination of left and right hand MI task effectively. Material and Methods In this analytical study, we estimated effective connectivity using Granger Causality (GC) methods namely, Generalized Partial Directed Coherence (GPDC), Directed Transfer Function (DTF) and direct Directed Transfer Function (dDTF). These measures determine the transient causal relation between different brain areas. Then a feature subset selection method based on Kruskal-Wallis test was performed to choose most significant directed causal connection between channels. Moreover, the minimal-redundancy-maximal-relevance feature selection method is applied to discard non-significance features. Finally, support vector machine method is used for classification. Results The maximum value of the classification accuracies using GC methods over different frequency bands in 29 subjects in 60 trial is approximately 84% in Mu (8-12 Hz) - Beta1 (12 - 15 Hz) frequency band using GPDC method. Conclusion This new hierarchical automated BCI system could be applied for discrimination of left and right hand MI tasks from EEG signal, effectively.
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Affiliation(s)
- Arash Maghsoudi
- PhD, Department of Biomedical Engineering, Science and Research Branch, Islamic Azad University, Tehran, Iran
| | - Ahmad Shalbaf
- PhD, Department of Biomedical Engineering and Medical Physics, School of Medicine, Shahid Beheshti University of Medical Sciences, Tehran, Iran
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Souza RHCE, Naves ELM. Attention Detection in Virtual Environments Using EEG Signals: A Scoping Review. Front Physiol 2021; 12:727840. [PMID: 34887770 PMCID: PMC8650681 DOI: 10.3389/fphys.2021.727840] [Citation(s) in RCA: 10] [Impact Index Per Article: 3.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/19/2021] [Accepted: 10/25/2021] [Indexed: 11/25/2022] Open
Abstract
The competitive demand for attention is present in our daily lives, and the identification of neural processes in the EEG signals associated with the demand for specific attention can be useful to the individual’s interactions in virtual environments. Since EEG-based devices can be portable, non-invasive, and present high temporal resolution technology for recording neural signal, the interpretations of virtual systems user’s attention, fatigue and cognitive load based on parameters extracted from the EEG signal are relevant for several purposes, such as games, rehabilitation, and therapies. However, despite the large amount of studies on this subject, different methodological forms are highlighted and suggested in this work, relating virtual environments, demand of attention, workload and fatigue applications. In our summarization, we discuss controversies, current research gaps and future directions together with the background and final sections.
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Affiliation(s)
- Rhaíra Helena Caetano E Souza
- Assistive Technology Laboratory, Electrical Engineering Faculty, Federal University of Uberlândia, Uberlândia, Brazil.,Federal Institute of Education, Science and Technology of Brasília, Brasília, Brazil
| | - Eduardo Lázaro Martins Naves
- Assistive Technology Laboratory, Electrical Engineering Faculty, Federal University of Uberlândia, Uberlândia, Brazil
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5
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Kumar S, Tsunoda T, Sharma A. SPECTRA: a tool for enhanced brain wave signal recognition. BMC Bioinformatics 2021; 22:195. [PMID: 34078274 PMCID: PMC8170968 DOI: 10.1186/s12859-021-04091-x] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/15/2021] [Accepted: 03/21/2021] [Indexed: 12/31/2022] Open
Abstract
Background Brain wave signal recognition has gained increased attention in neuro-rehabilitation applications. This has driven the development of brain–computer interface (BCI) systems. Brain wave signals are acquired using electroencephalography (EEG) sensors, processed and decoded to identify the category to which the signal belongs. Once the signal category is determined, it can be used to control external devices. However, the success of such a system essentially relies on significant feature extraction and classification algorithms. One of the commonly used feature extraction technique for BCI systems is common spatial pattern (CSP). Results The performance of the proposed spatial-frequency-temporal feature extraction (SPECTRA) predictor is analysed using three public benchmark datasets. Our proposed predictor outperformed other competing methods achieving lowest average error rates of 8.55%, 17.90% and 20.26%, and highest average kappa coefficient values of 0.829, 0.643 and 0.595 for BCI Competition III dataset IVa, BCI Competition IV dataset I and BCI Competition IV dataset IIb, respectively.
Conclusions Our proposed SPECTRA predictor effectively finds features that are more separable and shows improvement in brain wave signal recognition that can be instrumental in developing improved real-time BCI systems that are computationally efficient.
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Affiliation(s)
- Shiu Kumar
- School of Electrical and Electronics Engineering, Fiji National University, Suva, Fiji.
| | - Tatsuhiko Tsunoda
- Laboratory for Medical Science Mathematics, RIKEN Center for Integrative Medical Sciences, Yokohama, 230-0045, Japan.,Department of Medical Science Mathematics, Medical Research Institute, Tokyo Medical and Dental University (TMDU), Tokyo, 113-8510, Japan.,Laboratory for Medical Science Mathematics, Department of Biological Sciences, Graduate School of Science, University of Tokyo, Tokyo, 113-0033, Japan
| | - Alok Sharma
- Laboratory for Medical Science Mathematics, RIKEN Center for Integrative Medical Sciences, Yokohama, 230-0045, Japan.,Department of Medical Science Mathematics, Medical Research Institute, Tokyo Medical and Dental University (TMDU), Tokyo, 113-8510, Japan.,School of Engineering and Physics, The University of the South Pacific, Suva, Fiji.,Institute for Integrated and Intelligent Systems, Griffith University, Nathan, Brisbane, QLD, Australia
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Singh A, Hussain AA, Lal S, Guesgen HW. A Comprehensive Review on Critical Issues and Possible Solutions of Motor Imagery Based Electroencephalography Brain-Computer Interface. SENSORS 2021; 21:s21062173. [PMID: 33804611 PMCID: PMC8003721 DOI: 10.3390/s21062173] [Citation(s) in RCA: 36] [Impact Index Per Article: 12.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 12/28/2020] [Revised: 03/15/2021] [Accepted: 03/16/2021] [Indexed: 01/16/2023]
Abstract
Motor imagery (MI) based brain–computer interface (BCI) aims to provide a means of communication through the utilization of neural activity generated due to kinesthetic imagination of limbs. Every year, a significant number of publications that are related to new improvements, challenges, and breakthrough in MI-BCI are made. This paper provides a comprehensive review of the electroencephalogram (EEG) based MI-BCI system. It describes the current state of the art in different stages of the MI-BCI (data acquisition, MI training, preprocessing, feature extraction, channel and feature selection, and classification) pipeline. Although MI-BCI research has been going for many years, this technology is mostly confined to controlled lab environments. We discuss recent developments and critical algorithmic issues in MI-based BCI for commercial deployment.
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7
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Apicella A, Arpaia P, Frosolone M, Moccaldi N. High-wearable EEG-based distraction detection in motor rehabilitation. Sci Rep 2021; 11:5297. [PMID: 33674657 PMCID: PMC7935996 DOI: 10.1038/s41598-021-84447-8] [Citation(s) in RCA: 15] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/15/2020] [Accepted: 02/03/2021] [Indexed: 11/09/2022] Open
Abstract
A method for EEG-based distraction detection during motor-rehabilitation tasks is proposed. A wireless cap guarantees very high wearability with dry electrodes and a low number of channels. Experimental validation is performed on a dataset from 17 volunteers. Different feature extractions from spatial, temporal, and frequency domain and classification strategies were evaluated. The performances of five supervised classifiers in discriminating between attention on pure movement and with distractors were compared. A k-Nearest Neighbors classifier achieved an accuracy of 92.8 ± 1.6%. In this last case, the feature extraction is based on a custom 12 pass-band Filter-Bank (FB) and the Common Spatial Pattern (CSP) algorithm. In particular, the mean Recall of classification (percentage of true positive in distraction detection) is higher than 92% and allows the therapist or an automated system to know when to stimulate the patient's attention for enhancing the therapy effectiveness.
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Affiliation(s)
- Andrea Apicella
- Department of Electrical Engineering and Information Technology, University of Naples Federico II, Naples, Italy
| | - Pasquale Arpaia
- Department of Electrical Engineering and Information Technology, University of Naples Federico II, Naples, Italy. .,Interdepartmental Center for Research on Management and Innovation in Healthcare (CIRMIS), University of Naples Federico II, Naples, Italy.
| | - Mirco Frosolone
- Department of Public Health and Preventive Medicine, University of Naples Federico II, Naples, Italy
| | - Nicola Moccaldi
- Department of Electrical Engineering and Information Technology, University of Naples Federico II, Naples, Italy
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Shamsi F, Haddad A, Najafizadeh L. Early classification of motor tasks using dynamic functional connectivity graphs from EEG. J Neural Eng 2020; 18. [PMID: 33246319 DOI: 10.1088/1741-2552/abce70] [Citation(s) in RCA: 10] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/06/2020] [Accepted: 11/27/2020] [Indexed: 12/13/2022]
Abstract
OBJECTIVE Classification of electroencephalography (EEG) signals with high accuracy using short recording intervals has been a challenging problem in developing brain computer interfaces (BCIs). This paper presents a novel feature extraction method for EEG recordings to tackle this problem. APPROACH The proposed approach is based on the concept that the brain functions in a dynamic manner, and utilizes dynamic functional connectivity graphs. The EEG data is first segmented into intervals during which functional networks sustain their connectivity. Functional connectivity networks for each identified segment are then localized, and graphs are constructed, which will be used as features. To take advantage of the dynamic nature of the generated graphs, a Long Short Term Memory (LSTM) classifier is employed for classification. MAIN RESULTS Features extracted from various durations of post-stimulus EEG data associated with motor execution and imagery tasks are used to test the performance of the classifier. Results show an average accuracy of 85.32% using features extracted from only 500 ms of the post-stimulus data. SIGNIFICANCE Our results demonstrate, for the first time, that using the proposed feature extraction method, it is possible to classify motor tasks from EEG recordings using a short interval of the data in the order of hundreds of milliseconds (e.g. 500 ms). This duration is considerably shorter than what has been reported before. These results will have significant implications for improving the effectiveness and the speed of BCIs, particularly for those used in assistive technologies.
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Affiliation(s)
- Foroogh Shamsi
- Electrical and Computer Engineering, Rutgers University, 94 Brett Rd, New Brunswick, New Jersey, NJ 08854, UNITED STATES
| | - Ali Haddad
- Electrical and Computer Engineering, Rutgers University, 94 Brett Rd, New Brunswick, New Jersey, NJ 08854, UNITED STATES
| | - Laleh Najafizadeh
- Electrical and Computer Engineering, Rutgers University, 94 Brett Rd, New Brunswick, New Jersey, 08901-8554, UNITED STATES
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9
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Brain-computer interface for human-multirobot strategic consensus with a differential world model. APPL INTELL 2020. [DOI: 10.1007/s10489-020-01963-2] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/23/2022]
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10
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Rodrigues PG, Filho CAS, Attux R, Castellano G, Soriano DC. Space-time recurrences for functional connectivity evaluation and feature extraction in motor imagery brain-computer interfaces. Med Biol Eng Comput 2019; 57:1709-1725. [PMID: 31127535 DOI: 10.1007/s11517-019-01989-w] [Citation(s) in RCA: 19] [Impact Index Per Article: 3.8] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/01/2018] [Accepted: 05/03/2019] [Indexed: 12/18/2022]
Abstract
This work presents a classification performance comparison between different frameworks for functional connectivity evaluation and complex network feature extraction aiming to distinguish motor imagery classes in electroencephalography (EEG)-based brain-computer interfaces (BCIs). The analysis was performed in two online datasets: (1) a classical benchmark-the BCI competition IV dataset 2a-allowing a comparison with a representative set of strategies previously employed in this BCI paradigm and (2) a statistically representative dataset for signal processing technique comparisons over 52 subjects. Besides exploring three classical similarity measures-Pearson correlation, Spearman correlation, and mean phase coherence-this work also proposes a recurrence-based alternative for estimating EEG brain functional connectivity, which takes into account the recurrence density between pairwise electrodes over a time window. These strategies were followed by graph feature evaluation considering clustering coefficient, degree, betweenness centrality, and eigenvector centrality. The features were selected by Fisher's discriminating ratio and classification was performed by a least squares classifier in agreement with classical and online BCI processing strategies. The results revealed that the recurrence-based approach for functional connectivity evaluation was significantly better than the other frameworks, which is probably associated with the use of higher order statistics underlying the electrode joint probability estimation and a higher capability of capturing nonlinear inter-relations. There were no significant differences in performance among the evaluated graph features, but the eigenvector centrality was the best feature regarding processing time. Finally, the best ranked graph-based attributes were found in classical EEG motor cortex positions for the subjects with best performances, relating functional organization and motor activity. Graphical Abstract Evaluating functional connectivity based on Space-Time Recurrence Counting for motor imagery classification in brain-computer interfaces. Recurrences are evaluated between electrodes over a time window, and, after a density threshold, the electrodes adjacency matrix is stablish, leading to a graph. Graph-based topological measures are used for motor imagery classification.
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Affiliation(s)
- Paula G Rodrigues
- Engineering, Modeling and Applied Social Sciences Center (CECS), Federal University of ABC (UFABC), São Bernardo do Campo, SP, Brazil.
- Brazilian Institute of Neuroscience and Neurotechnology (BRAINN), Campinas, SP, Brazil.
| | - Carlos A Stefano Filho
- Neurophysics Group, Institute of Physics Gleb Wataghin (IFGW), University of Campinas (UNICAMP), Campinas, SP, Brazil
- Brazilian Institute of Neuroscience and Neurotechnology (BRAINN), Campinas, SP, Brazil
| | - Romis Attux
- School of Electrical and Computer Engineering (FEEC), UNICAMP, Campinas, SP, Brazil
- Brazilian Institute of Neuroscience and Neurotechnology (BRAINN), Campinas, SP, Brazil
| | - Gabriela Castellano
- Neurophysics Group, Institute of Physics Gleb Wataghin (IFGW), University of Campinas (UNICAMP), Campinas, SP, Brazil
- Brazilian Institute of Neuroscience and Neurotechnology (BRAINN), Campinas, SP, Brazil
| | - Diogo C Soriano
- Engineering, Modeling and Applied Social Sciences Center (CECS), Federal University of ABC (UFABC), São Bernardo do Campo, SP, Brazil
- Brazilian Institute of Neuroscience and Neurotechnology (BRAINN), Campinas, SP, Brazil
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Santamaria L, James C. Using brain connectivity metrics from synchrostates to perform motor imagery classification in EEG-based BCI systems. Healthc Technol Lett 2018; 5:88-93. [PMID: 29922477 PMCID: PMC5998754 DOI: 10.1049/htl.2017.0049] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.2] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/04/2017] [Revised: 12/26/2017] [Accepted: 02/05/2018] [Indexed: 11/20/2022] Open
Abstract
Phase synchronisation between different neural groups is considered an important source of information to understand the underlying mechanisms of brain cognition. This Letter investigated phase-synchronisation patterns from electroencephalogram (EEG) signals recorded from ten healthy participants performing motor imagery (MI) tasks using schematic emotional faces as stimuli. These phase-synchronised states, named synchrostates, are specific for each cognitive task performed by the user. The maximum and minimum number of occurrence states were selected for each subject and task to extract the connectivity network measures based on graph theory to feed a set of classification algorithms. Two MI tasks were successfully classified with the highest accuracy of 85% with corresponding sensitivity and specificity of 85%. In this work, not only the performance of different supervised learning techniques was studied, as well as the optimal subset of features to obtain the best discrimination rates. The robustness of this classification method for MI tasks indicates the possibility of expanding its use for online classification of the brain-computer interface (BCI) systems.
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Affiliation(s)
- Lorena Santamaria
- Institute of Digital Healthcare, Warwick Manufacturing Group, University of Warwick, Coventry, CV4 7AL, UK
| | - Christopher James
- Warwick Engineering in Biomedicine, School of Engineering, University of Warwick, Coventry, CV4 7AL, UK
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Stefano Filho CA, Attux R, Castellano G. Can graph metrics be used for EEG-BCIs based on hand motor imagery? Biomed Signal Process Control 2018. [DOI: 10.1016/j.bspc.2017.09.026] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.2] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/18/2022]
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13
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Kumar S, Mamun K, Sharma A. CSP-TSM: Optimizing the performance of Riemannian tangent space mapping using common spatial pattern for MI-BCI. Comput Biol Med 2017; 91:231-242. [PMID: 29100117 DOI: 10.1016/j.compbiomed.2017.10.025] [Citation(s) in RCA: 51] [Impact Index Per Article: 7.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/19/2017] [Revised: 10/08/2017] [Accepted: 10/23/2017] [Indexed: 11/18/2022]
Abstract
BACKGROUND Classification of electroencephalography (EEG) signals for motor imagery based brain computer interface (MI-BCI) is an exigent task and common spatial pattern (CSP) has been extensively explored for this purpose. In this work, we focused on developing a new framework for classification of EEG signals for MI-BCI. METHOD We propose a single band CSP framework for MI-BCI that utilizes the concept of tangent space mapping (TSM) in the manifold of covariance matrices. The proposed method is named CSP-TSM. Spatial filtering is performed on the bandpass filtered MI EEG signal. Riemannian tangent space is utilized for extracting features from the spatial filtered signal. The TSM features are then fused with the CSP variance based features and feature selection is performed using Lasso. Linear discriminant analysis (LDA) is then applied to the selected features and finally classification is done using support vector machine (SVM) classifier. RESULTS The proposed framework gives improved performance for MI EEG signal classification in comparison with several competing methods. Experiments conducted shows that the proposed framework reduces the overall classification error rate for MI-BCI by 3.16%, 5.10% and 1.70% (for BCI Competition III dataset IVa, BCI Competition IV Dataset I and BCI Competition IV Dataset IIb, respectively) compared to the conventional CSP method under the same experimental settings. CONCLUSION The proposed CSP-TSM method produces promising results when compared with several competing methods in this paper. In addition, the computational complexity is less compared to that of TSM method. Our proposed CSP-TSM framework can be potentially used for developing improved MI-BCI systems.
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Affiliation(s)
- Shiu Kumar
- Department of Electronics, Instrumentation and Control, School of Electrical & Electronics Engineering, College of Engineering, Science and Technology, Fiji National University, Suva, Fiji; School of Engineering and Physics, Faculty of Science, Technology and Environment, The University of the South Pacific, Suva, Fiji.
| | - Kabir Mamun
- School of Engineering and Physics, Faculty of Science, Technology and Environment, The University of the South Pacific, Suva, Fiji.
| | - Alok Sharma
- School of Engineering and Physics, Faculty of Science, Technology and Environment, The University of the South Pacific, Suva, Fiji; Institute for Integrated and Intelligent Systems (IIIS), Griffith University, Brisbane, Australia; RIKEN Center for Integrative Medical Sciences, Yokohama 230-0045, Japan.
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15
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Beveridge R, Wilson S, Coyle D. 3D graphics, virtual reality, and motion-onset visual evoked potentials in neurogaming. PROGRESS IN BRAIN RESEARCH 2016; 228:329-53. [PMID: 27590974 DOI: 10.1016/bs.pbr.2016.06.006] [Citation(s) in RCA: 12] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 12/12/2022]
Abstract
A brain-computer interface (BCI) offers movement-free control of a computer application and is achieved by reading and translating the cortical activity of the brain into semantic control signals. Motion-onset visual evoked potentials (mVEP) are neural potentials employed in BCIs and occur when motion-related stimuli are attended visually. mVEP dynamics are correlated with the position and timing of the moving stimuli. To investigate the feasibility of utilizing the mVEP paradigm with video games of various graphical complexities including those of commercial quality, we conducted three studies over four separate sessions comparing the performance of classifying five mVEP responses with variations in graphical complexity and style, in-game distractions, and display parameters surrounding mVEP stimuli. To investigate the feasibility of utilizing contemporary presentation modalities in neurogaming, one of the studies compared mVEP classification performance when stimuli were presented using the oculus rift virtual reality headset. Results from 31 independent subjects were analyzed offline. The results show classification performances ranging up to 90% with variations in conditions in graphical complexity having limited effect on mVEP performance; thus, demonstrating the feasibility of using the mVEP paradigm within BCI-based neurogaming.
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Affiliation(s)
- R Beveridge
- Intelligent Systems Research Centre, Ulster University, Derry, Northern Ireland, United Kingdom.
| | - S Wilson
- Intelligent Systems Research Centre, Ulster University, Derry, Northern Ireland, United Kingdom
| | - D Coyle
- Intelligent Systems Research Centre, Ulster University, Derry, Northern Ireland, United Kingdom
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