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Meenakshinathan J, Gupta V, Reddy TK, Behera L, Sandhan T. Session-independent subject-adaptive mental imagery BCI using selective filter-bank adaptive Riemannian features. Med Biol Eng Comput 2024:10.1007/s11517-024-03137-5. [PMID: 38825665 DOI: 10.1007/s11517-024-03137-5] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/17/2023] [Accepted: 05/23/2024] [Indexed: 06/04/2024]
Abstract
The brain-computer interfaces (BCIs) facilitate the users to exploit information encoded in neural signals, specifically electroencephalogram (EEG), to control devices and for neural rehabilitation. Mental imagery (MI)-driven BCI predicts the user's pre-meditated mental objectives, which could be deployed as command signals. This paper presents a novel learning-based framework for classifying MI tasks using EEG-based BCI. In particular, our work focuses on the variation in inter-session data and the extraction of multi-spectral user-tailored features for robust performance. Thus, the goal is to create a calibration-free subject-adaptive learning framework for various mental imagery tasks not restricted to motor imagery alone. In this regard, critical spectral bands and the best temporal window are first selected from the EEG training trials of the subject based on the Riemannian user learning distance metric (Dscore) that checks for distinct and stable patterns. The filtered covariance matrices of the EEG trials in each spectral band are then transformed towards a reference covariance matrix using the Riemannian transfer learning, enabling the different sessions to be comparable. The evaluation of our proposed Selective Time-window and Multi-scale Filter-Bank with Adaptive Riemannian (STFB-AR) features on four public datasets, including disabled subjects, showed around 15% and 8% improvement in mean accuracy over baseline and fixed filter-bank models, respectively.
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Affiliation(s)
| | - Vinay Gupta
- Department of Electrical Engineering, IIT Kanpur, Kanpur, India
| | | | - Laxmidhar Behera
- Department of Electrical Engineering, IIT Kanpur, Kanpur, India
- IIT Mandi, Mandi, India
| | - Tushar Sandhan
- Department of Electrical Engineering, IIT Kanpur, Kanpur, India
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2
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Pan L, Wang K, Xu L, Sun X, Yi W, Xu M, Ming D. Riemannian geometric and ensemble learning for decoding cross-session motor imagery electroencephalography signals. J Neural Eng 2023; 20:066011. [PMID: 37931299 DOI: 10.1088/1741-2552/ad0a01] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/27/2023] [Accepted: 11/06/2023] [Indexed: 11/08/2023]
Abstract
Objective.Brain-computer interfaces (BCIs) enable a direct communication pathway between the human brain and external devices, without relying on the traditional peripheral nervous and musculoskeletal systems. Motor imagery (MI)-based BCIs have attracted significant interest for their potential in motor rehabilitation. However, current algorithms fail to account for the cross-session variability of electroencephalography signals, limiting their practical application.Approach.We proposed a Riemannian geometry-based adaptive boosting and voting ensemble (RAVE) algorithm to address this issue. Our approach segmented the MI period into multiple sub-datasets using a sliding window approach and extracted features from each sub-dataset using Riemannian geometry. We then trained adaptive boosting (AdaBoost) ensemble learning classifiers for each sub-dataset, with the final BCI output determined by majority voting of all classifiers. We tested our proposed RAVE algorithm and eight other competing algorithms on four datasets (Pan2023, BNCI001-2014, BNCI001-2015, BNCI004-2015).Main results.Our results showed that, in the cross-session scenario, the RAVE algorithm outperformed the eight other competing algorithms significantly under different within-session training sample sizes. Compared to traditional algorithms that involved a large number of training samples, the RAVE algorithm achieved similar or even better classification performance on the datasets (Pan2023, BNCI001-2014, BNCI001-2015), even when it did not use or only used a small number of within-session training samples.Significance.These findings indicate that our cross-session decoding strategy could enable MI-BCI applications that require no or minimal training process.
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Affiliation(s)
- Lincong Pan
- Academy of Medical Engineering and Translational Medicine, Tianjin University, Tianjin 300072, People's Republic of China
- School of Precision Instruments and Optoelectronics Engineering, Tianjin University, Tianjin 300072, People's Republic of China
| | - Kun Wang
- Academy of Medical Engineering and Translational Medicine, Tianjin University, Tianjin 300072, People's Republic of China
- Haihe Laboratory of Brain-computer Interaction and Human-machine Integration, Tianjin 300392, People's Republic of China
| | - Lichao Xu
- Academy of Medical Engineering and Translational Medicine, Tianjin University, Tianjin 300072, People's Republic of China
| | - Xinwei Sun
- Academy of Medical Engineering and Translational Medicine, Tianjin University, Tianjin 300072, People's Republic of China
- School of Precision Instruments and Optoelectronics Engineering, Tianjin University, Tianjin 300072, People's Republic of China
| | - Weibo Yi
- Beijing Machine and Equipment Institute, Beijing 100192, People's Republic of China
| | - Minpeng Xu
- Academy of Medical Engineering and Translational Medicine, Tianjin University, Tianjin 300072, People's Republic of China
- School of Precision Instruments and Optoelectronics Engineering, Tianjin University, Tianjin 300072, People's Republic of China
- Haihe Laboratory of Brain-computer Interaction and Human-machine Integration, Tianjin 300392, People's Republic of China
| | - Dong Ming
- Academy of Medical Engineering and Translational Medicine, Tianjin University, Tianjin 300072, People's Republic of China
- School of Precision Instruments and Optoelectronics Engineering, Tianjin University, Tianjin 300072, People's Republic of China
- Haihe Laboratory of Brain-computer Interaction and Human-machine Integration, Tianjin 300392, People's Republic of China
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Friedrich EVC, Neuper C, Scherer R. Editorial: Mind over brain, brain over mind: cognitive causes and consequences of controlling brain activity - volume II. Front Hum Neurosci 2023; 17:1280095. [PMID: 37810762 PMCID: PMC10552517 DOI: 10.3389/fnhum.2023.1280095] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/19/2023] [Accepted: 08/22/2023] [Indexed: 10/10/2023] Open
Affiliation(s)
- Elisabeth V. C. Friedrich
- Research Unit Biological Psychology, Department of Psychology, Ludwig-Maximilians-Universität München, Munich, Germany
| | - Christa Neuper
- Neuropsychology, Department of Psychology, University of Graz, Graz, Austria
| | - Reinhold Scherer
- Brain-Computer Interfaces and Neural Engineering Laboratory, School of Computer Science and Electronic Engineering, University of Essex, Colchester, United Kingdom
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EEG-Based Mental Tasks Recognition via a Deep Learning-Driven Anomaly Detector. Diagnostics (Basel) 2022; 12:diagnostics12122984. [PMID: 36552991 PMCID: PMC9776901 DOI: 10.3390/diagnostics12122984] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/22/2022] [Revised: 11/20/2022] [Accepted: 11/24/2022] [Indexed: 12/05/2022] Open
Abstract
This paper introduces an unsupervised deep learning-driven scheme for mental tasks' recognition using EEG signals. To this end, the Multichannel Wiener filter was first applied to EEG signals as an artifact removal algorithm to achieve robust recognition. Then, a quadratic time-frequency distribution (QTFD) was applied to extract effective time-frequency signal representation of the EEG signals and catch the EEG signals' spectral variations over time to improve the recognition of mental tasks. The QTFD time-frequency features are employed as input for the proposed deep belief network (DBN)-driven Isolation Forest (iF) scheme to classify the EEG signals. Indeed, a single DBN-based iF detector is constructed based on each class's training data, with the class's samples as inliers and all other samples as anomalies (i.e., one-vs.-rest). The DBN is considered to learn pertinent information without assumptions on the data distribution, and the iF scheme is used for data discrimination. This approach is assessed using experimental data comprising five mental tasks from a publicly available database from the Graz University of Technology. Compared to the DBN-based Elliptical Envelope, Local Outlier Factor, and state-of-the-art EEG-based classification methods, the proposed DBN-based iF detector offers superior discrimination performance of mental tasks.
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Chen L, Yu Z, Yang J. SPD-CNN: A plain CNN-based model using the symmetric positive definite matrices for cross-subject EEG classification with meta-transfer-learning. Front Neurorobot 2022; 16:958052. [PMID: 35990886 PMCID: PMC9383414 DOI: 10.3389/fnbot.2022.958052] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/31/2022] [Accepted: 07/04/2022] [Indexed: 11/13/2022] Open
Abstract
The electroencephalography (EEG) signals are easily contaminated by various artifacts and noise, which induces a domain shift in each subject and significant pattern variability among different subjects. Therefore, it hinders the improvement of EEG classification accuracy in the cross-subject learning scenario. Convolutional neural networks (CNNs) have been extensively applied to EEG-based Brain-Computer Interfaces (BCIs) by virtue of the capability of performing automatic feature extraction and classification. However, they have been mainly applied to the within-subject classification which would consume lots of time for training and calibration. Thus, it limits the further applications of CNNs in BCIs. In order to build a robust classification algorithm for a calibration-less BCI system, we propose an end-to-end model that transforms the EEG signals into symmetric positive definite (SPD) matrices and captures the features of SPD matrices by using a CNN. To avoid the time-consuming calibration and ensure the application of the proposed model, we use the meta-transfer-learning (MTL) method to learn the essential features from different subjects. We validate our model by making extensive experiments on three public motor-imagery datasets. The experimental results demonstrate the effectiveness of our proposed method in the cross-subject learning scenario.
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Affiliation(s)
- Lezhi Chen
- College of Automation Science and Engineering, South China University of Technology, Guangzhou, China
| | - Zhuliang Yu
- College of Automation Science and Engineering, South China University of Technology, Guangzhou, China
- Pazhou Laboratory, Guangzhou, China
- *Correspondence: Zhuliang Yu
| | - Jian Yang
- College of Automation Science and Engineering, South China University of Technology, Guangzhou, China
- Jian Yang
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Wei Y, Li J, Ji H, Jin L, Liu L, Bai Z, Ye C. A Semi-Supervised Progressive Learning Algorithm for Brain-Computer Interface. IEEE Trans Neural Syst Rehabil Eng 2022; 30:2067-2076. [PMID: 35853068 DOI: 10.1109/tnsre.2022.3192448] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/09/2022]
Abstract
Brain-computer interface (BCI) usually suffers from the problem of low recognition accuracy and large calibration time, especially when identifying motor imagery tasks for subjects with indistinct features and classifying fine grained motion control tasks by electroencephalogram (EEG)-electromyogram (EMG) fusion analysis. To fill the research gap, this paper presents an end-to-end semi-supervised learning framework for EEG classification and EEG-EMG fusion analysis. Benefiting from the proposed metric learning based label estimation strategy, sampling criterion and progressive learning scheme, the proposed framework efficiently extracts distinctive feature embedding from the unlabeled EEG samples and achieves a 5.40% improvement on BCI Competition IV Dataset IIa with 80% unlabeled samples and an average 3.35% improvement on two public BCI datasets. By employing synchronous EMG features as pseudo labels for the unlabeled EEG samples, the proposed framework further extracts deep level features of the synergistic complementarity between the EEG signals and EMG features based on the deep encoders, which improves the performance of hybrid BCI (with a 5.53% improvement for the Upper Limb Motion Dataset and an average 4.34% improvement on two hybrid datasets). Moreover, the ablation experiments show that the proposed framework can substantially improve the performance of the deep encoders (with an average 5.53% improvement). The proposed framework not only largely improves the performance of deep networks in the BCI system, but also significantly reduces the calibration time for EEG-EMG fusion analysis, which shows great potential for building an efficient and high-performance hybrid BCI for the motor rehabilitation process.
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Corsi MC, Chevallier S, Fallani FDV, Yger F. Functional connectivity ensemble method to enhance BCI performance (FUCONE). IEEE Trans Biomed Eng 2022; 69:2826-2838. [PMID: 35226599 DOI: 10.1109/tbme.2022.3154885] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/06/2022]
Abstract
OBJECTIVE Relying on the idea that functional connectivity provides important insights on the underlying dynamic of neuronal interactions, we propose a novel framework that combines functional connectivity estimators and covariance-based pipelines to improve the classification of mental states, such as motor imagery. METHODS A Riemannian classifier is trained for each estimator and an ensemble classifier combines the decisions in each feature space. A thorough assessment of the functional connectivity estimators is provided and the best performing pipeline among those tested, called FUCONE, is evaluated on different conditions and datasets. RESULTS Using a meta-analysis to aggregate results across datasets, FUCONE performed significantly better than all state-of-the-art methods. CONCLUSION The performance gain is mostly imputable to the improved diversity of the feature spaces, increasing the robustness of the ensemble classifier with respect to the inter- and intra-subject variability. SIGNIFICANCE Our results offer new insights into the need to consider functional connectivity-based methods to improve the BCI performance.
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Altaheri H, Muhammad G, Alsulaiman M, Amin SU, Altuwaijri GA, Abdul W, Bencherif MA, Faisal M. Deep learning techniques for classification of electroencephalogram (EEG) motor imagery (MI) signals: a review. Neural Comput Appl 2021. [DOI: 10.1007/s00521-021-06352-5] [Citation(s) in RCA: 16] [Impact Index Per Article: 5.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/06/2023]
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Upregulation of Supplementary Motor Area Activation with fMRI Neurofeedback during Motor Imagery. eNeuro 2021; 8:ENEURO.0377-18.2020. [PMID: 33376115 PMCID: PMC7877466 DOI: 10.1523/eneuro.0377-18.2020] [Citation(s) in RCA: 8] [Impact Index Per Article: 2.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/27/2018] [Revised: 12/02/2020] [Accepted: 12/07/2020] [Indexed: 11/21/2022] Open
Abstract
Functional magnetic resonance imaging (fMRI) neurofeedback (NF) is a promising tool to study the relationship between behavior and brain activity. It enables people to self-regulate their brain signal. Here, we applied fMRI NF to train healthy participants to increase activity in their supplementary motor area (SMA) during a motor imagery (MI) task of complex body movements while they received a continuous visual feedback signal. This signal represented the activity of participants’ localized SMA regions in the NF group and a prerecorded signal in the control group (sham feedback). In the NF group only, results showed a gradual increase in SMA-related activity across runs. This upregulation was largely restricted to the SMA, while other regions of the motor network showed no, or only marginal NF effects. In addition, we found behavioral changes, i.e., shorter reaction times in a Go/No-go task after the NF training only. These results suggest that NF can assist participants to develop greater control over a specifically targeted motor region involved in motor skill learning. The results contribute to a better understanding of the underlying mechanisms of SMA NF based on MI with a direct implication for rehabilitation of motor dysfunctions.
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Roc A, Pillette L, Mladenovic J, Benaroch C, N'Kaoua B, Jeunet C, Lotte F. A review of user training methods in brain computer interfaces based on mental tasks. J Neural Eng 2020; 18. [PMID: 33181488 DOI: 10.1088/1741-2552/abca17] [Citation(s) in RCA: 28] [Impact Index Per Article: 7.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/24/2020] [Accepted: 11/12/2020] [Indexed: 12/12/2022]
Abstract
Mental-Tasks based Brain-Computer Interfaces (MT-BCIs) allow their users to interact with an external device solely by using brain signals produced through mental tasks. While MT-BCIs are promising for many applications, they are still barely used outside laboratories due to their lack of reliability. MT-BCIs require their users to develop the ability to self-regulate specific brain signals. However, the human learning process to control a BCI is still relatively poorly understood and how to optimally train this ability is currently under investigation. Despite their promises and achievements, traditional training programs have been shown to be sub-optimal and could be further improved. In order to optimize user training and improve BCI performance, human factors should be taken into account. An interdisciplinary approach should be adopted to provide learners with appropriate and/or adaptive training. In this article, we provide an overview of existing methods for MT-BCI user training - notably in terms of environment, instructions, feedback and exercises. We present a categorization and taxonomy of these training approaches, provide guidelines on how to choose the best methods and identify open challenges and perspectives to further improve MT-BCI user training.
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Affiliation(s)
| | | | | | - Camille Benaroch
- Inria Centre de recherche Bordeaux Sud-Ouest, Talence, 33405, FRANCE
| | - Bernard N'Kaoua
- Handicap, Activity, Cognition, Health, Inserm / University of Bordeaux, Talence, FRANCE
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Daly I, Rybar M. Neural component analysis: source localisation for motor imagery classification. ANNUAL INTERNATIONAL CONFERENCE OF THE IEEE ENGINEERING IN MEDICINE AND BIOLOGY SOCIETY. IEEE ENGINEERING IN MEDICINE AND BIOLOGY SOCIETY. ANNUAL INTERNATIONAL CONFERENCE 2020; 2020:498-501. [PMID: 33018036 DOI: 10.1109/embc44109.2020.9176690] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/06/2022]
Abstract
The electroencephalogram (EEG) records a summed mixture of multiple sources of neural activity distributed throughout the brain. Source separation methods aim to un-mix the EEG in order to recover activity generated by the original sources. However, most current state-of-the-art source separation methods do not take into account the physical locations of sources of EEG activity.We present a new source separation method which uses an accurate model of the head to un-mix the EEG into individual sources based on their physical locations.We apply our method to an EEG dataset recorded during motor imagery and show that it is able to identify sources that are located in distinct physical regions of the brain. We compare our method to independent component analysis and show that our sources have higher spatial specificity and, furthermore, allow higher classification accuracies (a mean improvement in accuracy of 8.6% was achieved p =0.039).
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Abstract
The electroencephalogram (EEG) was invented almost 100 years ago and is still a method of choice for many research questions, even applications-from functional brain imaging in neuroscientific investigations during movement to real-time applications like brain-computer interfacing. This chapter gives some background information on the establishment and properties of the EEG. This chapter starts with a closer look at the sources of EEG at a micro or neuronal level, followed by recording techniques, types of electrodes, and common EEG artifacts. Then an overview on EEG phenomena, namely, spontaneous EEG and event-related potentials build the middle part of this chapter. The last part discusses brain signals, which are used in current BCI research, including short descriptions and examples of applications.
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Affiliation(s)
- Gernot R Müller-Putz
- Institute for Neural Engineering, Laboratory of Brain-Computer Interfaces, Graz University of Technology, Graz, Austria.
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Yousefi R, Rezazadeh Sereshkeh A, Chau T. Online detection of error-related potentials in multi-class cognitive task-based BCIs. BRAIN-COMPUTER INTERFACES 2019. [DOI: 10.1080/2326263x.2019.1614770] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.4] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/26/2022]
Affiliation(s)
- Rozhin Yousefi
- Institute of Biomaterials and Biomedical Engineering, University of Toronto, Toronto, Canada
| | | | - Tom Chau
- Institute of Biomaterials and Biomedical Engineering, University of Toronto, Toronto, Canada
- Bloorview Research Institute, Holland Bloorview Hospital, Toronto, Canada
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Kalaganis FP, Laskaris NA, Chatzilari E, Nikolopoulos S, Kompatsiaris I. A Riemannian Geometry Approach to Reduced and Discriminative Covariance Estimation in Brain Computer Interfaces. IEEE Trans Biomed Eng 2019; 67:245-255. [PMID: 30998456 DOI: 10.1109/tbme.2019.2912066] [Citation(s) in RCA: 12] [Impact Index Per Article: 2.4] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/10/2022]
Abstract
OBJECTIVE Spatial covariance matrices are extensively employed as brain activity descriptors in brain computer interface (BCI) research that, typically, involve the whole array of sensors. Here, we introduce a methodological framework for delineating the subset of sensors, the covariance structure of which offers a reduced, but more powerful, representation of brain's coordination patterns that ultimately leads to reliable mind reading. METHODS Adopting a Riemannian geometry approach, we turn the problem of sensor selection as a maximization of a functional that is computed over the manifold of symmetric positive definite (SPD) matrices and encapsulates class separability in a way that facilitates the search among subsets of different size. The introduced optimization task, namely discriminative covariance reduction (DCR), lacks an analytical solution and is tackled via the cross-entropy optimization technique. RESULTS Based on two different EEG datasets and three distinct classification schemes, we demonstrate that the DCR approach provides a noteworthy gain in terms of accuracy (in some cases exceeding 20%) and a remarkable reduction in classification time (on average 82%). Additionally, results include the intriguing empirical finding that the pattern of selected sensors in the case of disabled persons depends on the type of disability. CONCLUSION The proposed DCR framework can speed up the classification time in BCI-systems operating on the SPD manifolds by simultaneously enhancing their reliability. This is achieved without sacrificing the neuroscientific interpretability endowed in the topographical arrangement of the selected sensors. SIGNIFICANCE Riemannian geometry is exploited for DCR in BCI systems, in a dimensionality-agnostic manner, guaranteeing improved performance.
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Padfield N, Zabalza J, Zhao H, Masero V, Ren J. EEG-Based Brain-Computer Interfaces Using Motor-Imagery: Techniques and Challenges. SENSORS 2019; 19:s19061423. [PMID: 30909489 PMCID: PMC6471241 DOI: 10.3390/s19061423] [Citation(s) in RCA: 153] [Impact Index Per Article: 30.6] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 01/30/2019] [Revised: 03/10/2019] [Accepted: 03/19/2019] [Indexed: 12/11/2022]
Abstract
Electroencephalography (EEG)-based brain-computer interfaces (BCIs), particularly those using motor-imagery (MI) data, have the potential to become groundbreaking technologies in both clinical and entertainment settings. MI data is generated when a subject imagines the movement of a limb. This paper reviews state-of-the-art signal processing techniques for MI EEG-based BCIs, with a particular focus on the feature extraction, feature selection and classification techniques used. It also summarizes the main applications of EEG-based BCIs, particularly those based on MI data, and finally presents a detailed discussion of the most prevalent challenges impeding the development and commercialization of EEG-based BCIs.
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Affiliation(s)
- Natasha Padfield
- Centre for Signal and Image Processing, University of Strathclyde, Glasgow G1 1XW, UK.
| | - Jaime Zabalza
- Centre for Signal and Image Processing, University of Strathclyde, Glasgow G1 1XW, UK.
| | - Huimin Zhao
- School of Computer Sciences, Guangdong Polytechnic Normal University, Guangzhou 510665, China.
- The Guangzhou Key Laboratory of Digital Content Processing and Security Technologies, Guangzhou 510665, China.
| | - Valentin Masero
- Department of Computer Systems and Telematics Engineering, Universidad de Extremadura, 06007 Badajoz, Spain.
| | - Jinchang Ren
- Centre for Signal and Image Processing, University of Strathclyde, Glasgow G1 1XW, UK.
- School of Electrical and Power Engineering, Taiyuan University of Technology, Taiyuan 030024, China.
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Grosselin F, Navarro-Sune X, Vozzi A, Pandremmenou K, De Vico Fallani F, Attal Y, Chavez M. Quality Assessment of Single-Channel EEG for Wearable Devices. SENSORS 2019; 19:s19030601. [PMID: 30709004 PMCID: PMC6387437 DOI: 10.3390/s19030601] [Citation(s) in RCA: 13] [Impact Index Per Article: 2.6] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 01/22/2019] [Accepted: 01/28/2019] [Indexed: 11/16/2022]
Abstract
The recent embedding of electroencephalographic (EEG) electrodes in wearable devices raises the problem of the quality of the data recorded in such uncontrolled environments. These recordings are often obtained with dry single-channel EEG devices, and may be contaminated by many sources of noise which can compromise the detection and characterization of the brain state studied. In this paper, we propose a classification-based approach to effectively quantify artefact contamination in EEG segments, and discriminate muscular artefacts. The performance of our method were assessed on different databases containing either artificially contaminated or real artefacts recorded with different type of sensors, including wet and dry EEG electrodes. Furthermore, the quality of unlabelled databases was evaluated. For all the studied databases, the proposed method is able to rapidly assess the quality of the EEG signals with an accuracy higher than 90%. The obtained performance suggests that our approach provide an efficient, fast and automated quality assessment of EEG signals from low-cost wearable devices typically composed of a dry single EEG channel.
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Affiliation(s)
- Fanny Grosselin
- hlSorbonne Université, UPMC Univ. Paris 06, INSERM U-1127, CNRS UMR-7225, Institut du Cerveau et de la Moelle Épinière (ICM), Groupe Hospitalier Pitié Salpêtrière-Charles Foix, 75013 Paris, France.
- myBrainTechnologies, 75010 Paris, France.
| | | | | | | | - Fabrizio De Vico Fallani
- hlSorbonne Université, UPMC Univ. Paris 06, INSERM U-1127, CNRS UMR-7225, Institut du Cerveau et de la Moelle Épinière (ICM), Groupe Hospitalier Pitié Salpêtrière-Charles Foix, 75013 Paris, France.
- INRIA, Aramis Project-Team, F-75013 Paris, France.
| | | | - Mario Chavez
- CNRS UMR-7225, Groupe Hospitalier Pitié-Salpêtrière-Charles Foix, 75013 Paris, France.
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Höller Y, Thomschewski A, Uhl A, Bathke AC, Nardone R, Leis S, Trinka E, Höller P. HD-EEG Based Classification of Motor-Imagery Related Activity in Patients With Spinal Cord Injury. Front Neurol 2018; 9:955. [PMID: 30510537 PMCID: PMC6252382 DOI: 10.3389/fneur.2018.00955] [Citation(s) in RCA: 5] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/20/2017] [Accepted: 10/24/2018] [Indexed: 12/16/2022] Open
Abstract
Brain computer interfaces (BCIs) are thought to revolutionize rehabilitation after SCI, e.g., by controlling neuroprostheses, exoskeletons, functional electrical stimulation, or a combination of these components. However, most BCI research was performed in healthy volunteers and it is unknown whether these results can be translated to patients with spinal cord injury because of neuroplasticity. We sought to examine whether high-density EEG (HD-EEG) could improve the performance of motor-imagery classification in patients with SCI. We recorded HD-EEG with 256 channels in 22 healthy controls and 7 patients with 14 recordings (4 patients had more than one recording) in an event related design. Participants were instructed acoustically to either imagine, execute, or observe foot and hand movements, or to rest. We calculated Fast Fourier Transform (FFT) and full frequency directed transfer function (ffDTF) for each condition and classified conditions pairwise with support vector machines when using only 2 channels over the sensorimotor area, full 10-20 montage, high-density montage of the sensorimotor cortex, and full HD-montage. Classification accuracies were comparable between patients and controls, with an advantage for controls for classifications that involved the foot movement condition. Full montages led to better results for both groups (p < 0.001), and classification accuracies were higher for FFT than for ffDTF (p < 0.001), for which the feature vector might be too long. However, full-montage 10–20 montage was comparable to high-density configurations. Motor-imagery driven control of neuroprostheses or BCI systems may perform as well in patients as in healthy volunteers with adequate technical configuration. We suggest the use of a whole-head montage and analysis of a broad frequency range.
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Affiliation(s)
- Yvonne Höller
- Department of Neurology, Christian Doppler Medical Centre and Centre for Cognitive Neuroscience, Paracelsus Medical University of Salzburg, Salzburg, Austria
| | - Aljoscha Thomschewski
- Department of Neurology, Christian Doppler Medical Centre and Centre for Cognitive Neuroscience, Paracelsus Medical University of Salzburg, Salzburg, Austria.,Spinal Cord Injury and Tissue Regeneration Center, Paracelsus Medical University of Salzburg, Salzburg, Austria
| | - Andreas Uhl
- Department of Computer Sciences, Paris-Lodron University of Salzburg, Salzburg, Austria
| | - Arne C Bathke
- Department of Mathematics, Paris-Lodron University of Salzburg, Salzburg, Austria
| | - Raffaele Nardone
- Department of Neurology, Christian Doppler Medical Centre and Centre for Cognitive Neuroscience, Paracelsus Medical University of Salzburg, Salzburg, Austria.,Spinal Cord Injury and Tissue Regeneration Center, Paracelsus Medical University of Salzburg, Salzburg, Austria.,Department of Neurology, Franz Tappeiner Hospital, Merano, Italy
| | - Stefan Leis
- Department of Neurology, Christian Doppler Medical Centre and Centre for Cognitive Neuroscience, Paracelsus Medical University of Salzburg, Salzburg, Austria.,Spinal Cord Injury and Tissue Regeneration Center, Paracelsus Medical University of Salzburg, Salzburg, Austria
| | - Eugen Trinka
- Department of Neurology, Christian Doppler Medical Centre and Centre for Cognitive Neuroscience, Paracelsus Medical University of Salzburg, Salzburg, Austria.,Spinal Cord Injury and Tissue Regeneration Center, Paracelsus Medical University of Salzburg, Salzburg, Austria
| | - Peter Höller
- Department of Neurology, Christian Doppler Medical Centre and Centre for Cognitive Neuroscience, Paracelsus Medical University of Salzburg, Salzburg, Austria.,Spinal Cord Injury and Tissue Regeneration Center, Paracelsus Medical University of Salzburg, Salzburg, Austria
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Feng J, Yin E, Jin J, Saab R, Daly I, Wang X, Hu D, Cichocki A. Towards correlation-based time window selection method for motor imagery BCIs. Neural Netw 2018; 102:87-95. [PMID: 29558654 DOI: 10.1016/j.neunet.2018.02.011] [Citation(s) in RCA: 91] [Impact Index Per Article: 15.2] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/12/2017] [Revised: 01/09/2018] [Accepted: 02/14/2018] [Indexed: 10/17/2022]
Abstract
The start of the cue is often used to initiate the feature window used to control motor imagery (MI)-based brain-computer interface (BCI) systems. However, the time latency during an MI period varies between trials for each participant. Fixing the starting time point of MI features can lead to decreased system performance in MI-based BCI systems. To address this issue, we propose a novel correlation-based time window selection (CTWS) algorithm for MI-based BCIs. Specifically, the optimized reference signals for each class were selected based on correlation analysis and performance evaluation. Furthermore, the starting points of time windows for both training and testing samples were adjusted using correlation analysis. Finally, the feature extraction and classification algorithms were used to calculate the classification accuracy. With two datasets, the results demonstrate that the CTWS algorithm significantly improved the system performance when compared to directly using feature extraction approaches. Importantly, the average improvement in accuracy of the CTWS algorithm on the datasets of healthy participants and stroke patients was 16.72% and 5.24%, respectively when compared to traditional common spatial pattern (CSP) algorithm. In addition, the average accuracy increased 7.36% and 9.29%, respectively when the CTWS was used in conjunction with Sub-Alpha-Beta Log-Det Divergences (Sub-ABLD) algorithm. These findings suggest that the proposed CTWS algorithm holds promise as a general feature extraction approach for MI-based BCIs.
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Affiliation(s)
- Jiankui Feng
- Key Laboratory of Advanced Control and Optimization for Chemical Processes, Ministry of Education, East China University of Science and Technology, Shanghai, PR China
| | - Erwei Yin
- National Institute of Defense Technology Innovation, Academy of Military Sciences China, Beijing, 100081, PR China
| | - Jing Jin
- Key Laboratory of Advanced Control and Optimization for Chemical Processes, Ministry of Education, East China University of Science and Technology, Shanghai, PR China.
| | - Rami Saab
- Key Laboratory of Advanced Control and Optimization for Chemical Processes, Ministry of Education, East China University of Science and Technology, Shanghai, PR China
| | - Ian Daly
- Brain-Computer Interfaces and Neural Engineering Laboratory, School of Computer Science and Electronic Engineering, University of Essex, Wivenhoe Park, Colchester, Essex, CO4 3SQ, UK
| | - Xingyu Wang
- Key Laboratory of Advanced Control and Optimization for Chemical Processes, Ministry of Education, East China University of Science and Technology, Shanghai, PR China
| | - Dewen Hu
- College of Mechatronic Engineering and Automation, National University of Defense Technology, Changsha, Hunan 410073, PR China
| | - Andrzej Cichocki
- Laboratory for Advanced Brain Signal Processing, Brain Science Institute, RIKEN, Wako-shi, Japan; Systems Research Institute PAS, Warsaw, Poland; Nicolaus Copernicus University (UMK), Torun, Poland
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20
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Statthaler K, Schwarz A, Steyrl D, Kobler R, Höller MK, Brandstetter J, Hehenberger L, Bigga M, Müller-Putz G. Cybathlon experiences of the Graz BCI racing team Mirage91 in the brain-computer interface discipline. J Neuroeng Rehabil 2017; 14:129. [PMID: 29282131 PMCID: PMC5745791 DOI: 10.1186/s12984-017-0344-9] [Citation(s) in RCA: 13] [Impact Index Per Article: 1.9] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/28/2017] [Accepted: 12/13/2017] [Indexed: 11/10/2022] Open
Abstract
BACKGROUND In this work, we share our experiences made at the world-wide first CYBATHLON, an event organized by the Eidgenössische Technische Hochschule Zürich (ETH Zürich), which took place in Zurich in October 2016. It is a championship for severely motor impaired people using assistive prototype devices to compete against each other. Our team, the Graz BCI Racing Team MIRAGE91 from Graz University of Technology, participated in the discipline "Brain-Computer Interface Race". A brain-computer interface (BCI) is a device facilitating control of applications via the user's thoughts. Prominent applications include assistive technology such as wheelchairs, neuroprostheses or communication devices. In the CYBATHLON BCI Race, pilots compete in a BCI-controlled computer game. METHODS We report on setting up our team, the BCI customization to our pilot including long term training and the final BCI system. Furthermore, we describe CYBATHLON participation and analyze our CYBATHLON result. RESULTS We found that our pilot was compliant over the whole time and that we could significantly reduce the average runtime between start and finish from initially 178 s to 143 s. After the release of the final championship specifications with shorter track length, the average runtime converged to 120 s. We successfully participated in the qualification race at CYBATHLON 2016, but performed notably worse than during training, with a runtime of 196 s. DISCUSSION We speculate that shifts in the features, due to the nonstationarities in the electroencephalogram (EEG), but also arousal are possible reasons for the unexpected result. Potential counteracting measures are discussed. CONCLUSIONS The CYBATHLON 2016 was a great opportunity for our student team. We consolidated our theoretical knowledge and turned it into practice, allowing our pilot to play a computer game. However, further research is required to make BCI technology invariant to non-task related changes of the EEG.
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Affiliation(s)
- Karina Statthaler
- Institute of Neural Engineering, Laboratory of Brain-Computer Interfaces, Graz University of Technology, 8010, Graz, Austria.,Graz BCI Racing Team MIRAGE91, Graz University of Technology, Graz, Austria
| | - Andreas Schwarz
- Institute of Neural Engineering, Laboratory of Brain-Computer Interfaces, Graz University of Technology, 8010, Graz, Austria.,Graz BCI Racing Team MIRAGE91, Graz University of Technology, Graz, Austria
| | - David Steyrl
- Institute of Neural Engineering, Laboratory of Brain-Computer Interfaces, Graz University of Technology, 8010, Graz, Austria.,Graz BCI Racing Team MIRAGE91, Graz University of Technology, Graz, Austria
| | - Reinmar Kobler
- Institute of Neural Engineering, Laboratory of Brain-Computer Interfaces, Graz University of Technology, 8010, Graz, Austria.,Graz BCI Racing Team MIRAGE91, Graz University of Technology, Graz, Austria
| | - Maria Katharina Höller
- Institute of Neural Engineering, Laboratory of Brain-Computer Interfaces, Graz University of Technology, 8010, Graz, Austria.,Graz BCI Racing Team MIRAGE91, Graz University of Technology, Graz, Austria
| | - Julia Brandstetter
- Graz BCI Racing Team MIRAGE91, Graz University of Technology, Graz, Austria
| | - Lea Hehenberger
- Institute of Neural Engineering, Laboratory of Brain-Computer Interfaces, Graz University of Technology, 8010, Graz, Austria.,Graz BCI Racing Team MIRAGE91, Graz University of Technology, Graz, Austria
| | - Marvin Bigga
- Graz BCI Racing Team MIRAGE91, Graz University of Technology, Graz, Austria
| | - Gernot Müller-Putz
- Institute of Neural Engineering, Laboratory of Brain-Computer Interfaces, Graz University of Technology, 8010, Graz, Austria. .,Graz BCI Racing Team MIRAGE91, Graz University of Technology, Graz, Austria.
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21
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Dhindsa K, Carcone D, Becker S. Toward an Open-Ended BCI: A User-Centered Coadaptive Design. Neural Comput 2017; 29:2742-2768. [PMID: 28777722 DOI: 10.1162/neco_a_01001] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/04/2022]
Abstract
Brain-computer interfaces (BCIs) allow users to control a device by interpreting their brain activity. For simplicity, these devices are designed to be operated by purposefully modulating specific predetermined neurophysiological signals, such as the sensorimotor rhythm. However, the ability to modulate a given neurophysiological signal is highly variable across individuals, contributing to the inconsistent performance of BCIs for different users. These differences suggest that individuals who experience poor BCI performance with one class of brain signals might have good results with another. In order to take advantage of individual abilities as they relate to BCI control, we need to move beyond the current approaches. In this letter, we explore a new BCI design aimed at a more individualized and user-focused experience, which we call open-ended BCI. Individual users were given the freedom to discover their own mental strategies as opposed to being trained to modulate a given brain signal. They then underwent multiple coadaptive training sessions with the BCI. Our first open-ended BCI performed similarly to comparable BCIs while accommodating a wider variety of mental strategies without a priori knowledge of the specific brain signals any individual might use. Post hoc analysis revealed individual differences in terms of which sensory modality yielded optimal performance. We found a large and significant effect of individual differences in background training and expertise, such as in musical training, on BCI performance. Future research should be focused on finding more generalized solutions to user training and brain state decoding methods to fully utilize the abilities of different individuals in an open-ended BCI. Accounting for each individual's areas of expertise could have important implications on BCI training and BCI application design.
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Affiliation(s)
- Kiret Dhindsa
- Neurotechnology and Neuroplasticity Lab, Department of Psychology, Neuroscience, and Behaviour, McMaster University, Hamilton, Ontario L8S 4L8, Canada
| | - Dean Carcone
- Neurotechnology and Neuroplasticity Lab, Department of Psychology, Neuroscience, and Behaviour, McMaster University, Hamilton, Ontario L8S 4L8, Canada
| | - Suzanna Becker
- Neurotechnology and Neuroplasticity Lab, Department of Psychology, Neuroscience, and Behaviour, McMaster University, Hamilton, Ontario L8S 4L8, Canada
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22
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Rathee D, Cecotti H, Prasad G. Single-trial effective brain connectivity patterns enhance discriminability of mental imagery tasks. J Neural Eng 2017; 14:056005. [PMID: 28597846 DOI: 10.1088/1741-2552/aa785c] [Citation(s) in RCA: 27] [Impact Index Per Article: 3.9] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/13/2023]
Abstract
OBJECTIVE The majority of the current approaches of connectivity based brain-computer interface (BCI) systems focus on distinguishing between different motor imagery (MI) tasks. Brain regions associated with MI are anatomically close to each other, hence these BCI systems suffer from low performances. Our objective is to introduce single-trial connectivity feature based BCI system for cognition imagery (CI) based tasks wherein the associated brain regions are located relatively far away as compared to those for MI. APPROACH We implemented time-domain partial Granger causality (PGC) for the estimation of the connectivity features in a BCI setting. The proposed hypothesis has been verified with two publically available datasets involving MI and CI tasks. MAIN RESULTS The results support the conclusion that connectivity based features can provide a better performance than a classical signal processing framework based on bandpass features coupled with spatial filtering for CI tasks, including word generation, subtraction, and spatial navigation. These results show for the first time that connectivity features can provide a reliable performance for imagery-based BCI system. SIGNIFICANCE We show that single-trial connectivity features for mixed imagery tasks (i.e. combination of CI and MI) can outperform the features obtained by current state-of-the-art method and hence can be successfully applied for BCI applications.
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Affiliation(s)
- Dheeraj Rathee
- Intelligent Systems Research Centre, School of Computing and Intelligent Systems, Ulster University, Derry-Londonderry, United Kingdom
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23
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Müller JS, Vidaurre C, Schreuder M, Meinecke FC, von Bünau P, Müller KR. A mathematical model for the two-learners problem. J Neural Eng 2017; 14:036005. [DOI: 10.1088/1741-2552/aa620b] [Citation(s) in RCA: 32] [Impact Index Per Article: 4.6] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/27/2023]
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Chavarriaga R, Fried-Oken M, Kleih S, Lotte F, Scherer R. Heading for new shores! Overcoming pitfalls in BCI design. BRAIN-COMPUTER INTERFACES 2016; 4:60-73. [PMID: 29629393 DOI: 10.1080/2326263x.2016.1263916] [Citation(s) in RCA: 53] [Impact Index Per Article: 6.6] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/22/2022]
Abstract
Research in brain-computer interfaces has achieved impressive progress towards implementing assistive technologies for restoration or substitution of lost motor capabilities, as well as supporting technologies for able-bodied subjects. Notwithstanding this progress, effective translation of these interfaces from proof-of concept prototypes into reliable applications remains elusive. As a matter of fact, most of the current BCI systems cannot be used independently for long periods of time by their intended end-users. Multiple factors that impair achieving this goal have already been identified. However, it is not clear how do they affect the overall BCI performance or how they should be tackled. This is worsened by the publication bias where only positive results are disseminated, preventing the research community from learning from its errors. This paper is the result of a workshop held at the 6th International BCI meeting in Asilomar. We summarize here the discussion on concrete research avenues and guidelines that may help overcoming common pitfalls and make BCIs become a useful alternative communication device.
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Affiliation(s)
- Ricardo Chavarriaga
- Defitech Chair in Brain-Machine Interface (CNBI), Center for Neuroprosthetics, Ecole Polytechnique Fédérale de Lausanne, Switzerland
| | - Melanie Fried-Oken
- Oregon Health & Science University, Institute on Development and Disability, Portland, Oregon USA
| | - Sonja Kleih
- Institute of Psychology, University of Würzburg, Marcusstraße 9-11, Würzburg, 97070, Germany
| | - Fabien Lotte
- Inria Bordeaux Sud-Ouest/LaBRI, 200 avenue de la vieille tour, 33405, Talence cedex, France
| | - Reinhold Scherer
- Institute of Neural Engineering, BCI-Lab, Graz University of Technology, Stremayrgasse 16/IV, 8010 Graz, Austria
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Scherer R, Faller J, Opisso E, Costa U, Steyrl D, Muller-Putz GR. Bring mental activity into action! An enhanced online co-adaptive brain-computer interface training protocol. ANNUAL INTERNATIONAL CONFERENCE OF THE IEEE ENGINEERING IN MEDICINE AND BIOLOGY SOCIETY. IEEE ENGINEERING IN MEDICINE AND BIOLOGY SOCIETY. ANNUAL INTERNATIONAL CONFERENCE 2016; 2015:2323-6. [PMID: 26736758 DOI: 10.1109/embc.2015.7318858] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.4] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/07/2022]
Abstract
Non-stationarity and inherent variability of the noninvasive electroencephalogram (EEG) makes robust recognition of spontaneous EEG patterns challenging. Reliable modulation of EEG patterns that a BCI can robustly detect is a skill that users must learn. In this paper, we present a novel online co-adaptive BCI training paradigm. The system autonomously screens users for their ability to modulate EEG patterns in a predictive way and adapts its model parameters online. Results of a supporting study in seven first-time BCI users with disability are very encouraging. Three of 7 users achieved online accuracy > 70% for 2-class BCI control after 24 minutes of training. Online performance in 6 of 7 users was significantly higher than chance level. Online control was based on one single bipolar EEG channel. Beta band activity carried most discriminant information. Our fully automatic co-adaptive online approach allows to evaluate whether user benefit from current BCI technology within a reasonable timescale.
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26
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Evaluation of induced and evoked changes in EEG during selective attention to verbal stimuli. J Neurosci Methods 2016; 270:165-176. [PMID: 27329006 DOI: 10.1016/j.jneumeth.2016.06.015] [Citation(s) in RCA: 6] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/02/2015] [Revised: 06/17/2016] [Accepted: 06/17/2016] [Indexed: 11/24/2022]
Abstract
BACKGROUND Two challenges need to be addressed before bringing non-motor mental tasks for brain-computer interface (BCI) control to persons in a minimally conscious state (MCS), who can be behaviorally unresponsive even when proven to be consciously aware: first, keeping the cognitive demands as low as possible so that they could be fulfilled by persons with MCS. Second, increasing the control of experimental protocol (i.e. type and timing of the task performance). NEW METHOD The goal of this study is twofold: first goal is to develop an experimental paradigm that can facilitate the performance of brain-teasers (e.g. mental subtraction and word generation) on the one hand, and can increase the control of experimental protocol on the other hand. The second goal of this study is to exploit the similar findings for mentally attending to someone else's verbal performance of brain-teaser tasks and self-performing the same tasks to setup an online BCI, and to compare it in healthy participants to the current "state-of-the-art" motor imagery (MI, sports). RESULTS The response accuracies for the best performing healthy participants indicate that selective attention to verbal performance of mental subtraction (SUB) is a viable alternative to the MI. Time-frequency analysis of the SUB task in one participant with MCS did not reveal any significant (p<0.05) EEG changes, whereas imagined performance of one sport of participants' choice (SPORT) revealed task-related EEG changes over neurophysiological plausible cortical areas. COMPARISON WITH EXISTING METHODS We found that mentally attending to someone else's verbal performance of brain-teaser tasks leads to similar results as in self-performing the same tasks. CONCLUSIONS In this work we demonstrated that a single auditory selective attention task (i.e. mentally attending to someone else's verbal performance of mental subtraction) can modulate both induced and evoked changes in EEG, and be used for yes/no communication in an auditory scanning paradigm.
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27
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Gibson RM, Owen AM, Cruse D. Brain-computer interfaces for patients with disorders of consciousness. PROGRESS IN BRAIN RESEARCH 2016; 228:241-91. [PMID: 27590972 DOI: 10.1016/bs.pbr.2016.04.003] [Citation(s) in RCA: 16] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 12/11/2022]
Abstract
The disorders of consciousness refer to clinical conditions that follow a severe head injury. Patients diagnosed as in a vegetative state lack awareness, while patients diagnosed as in a minimally conscious state retain fluctuating awareness. However, it is a challenge to accurately diagnose these disorders with clinical assessments of behavior. To improve diagnostic accuracy, neuroimaging-based approaches have been developed to detect the presence or absence of awareness in patients who lack overt responsiveness. For the small subset of patients who retain awareness, brain-computer interfaces could serve as tools for communication and environmental control. Here we review the existing literature concerning the sensory and cognitive abilities of patients with disorders of consciousness with respect to existing brain-computer interface designs. We highlight the challenges of device development for this special population and address some of the most promising approaches for future investigations.
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Affiliation(s)
- R M Gibson
- The Brain and Mind Institute, University of Western Ontario, London, ON, Canada; University of Western Ontario, London, ON, Canada.
| | - A M Owen
- The Brain and Mind Institute, University of Western Ontario, London, ON, Canada; University of Western Ontario, London, ON, Canada
| | - D Cruse
- School of Psychology, University of Birmingham, Birmingham, United Kingdom
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28
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Mateo S, Di Rienzo F, Bergeron V, Guillot A, Collet C, Rode G. Motor imagery reinforces brain compensation of reach-to-grasp movement after cervical spinal cord injury. Front Behav Neurosci 2015; 9:234. [PMID: 26441568 PMCID: PMC4566051 DOI: 10.3389/fnbeh.2015.00234] [Citation(s) in RCA: 21] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/18/2015] [Accepted: 08/19/2015] [Indexed: 01/19/2023] Open
Abstract
Individuals with cervical spinal cord injury (SCI) that causes tetraplegia are challenged with dramatic sensorimotor deficits. However, certain rehabilitation techniques may significantly enhance their autonomy by restoring reach-to-grasp movements. Among others, evidence of motor imagery (MI) benefits for neurological rehabilitation of upper limb movements is growing. This literature review addresses MI effectiveness during reach-to-grasp rehabilitation after tetraplegia. Among articles from MEDLINE published between 1966 and 2015, we selected ten studies including 34 participants with C4 to C7 tetraplegia and 22 healthy controls published during the last 15 years. We found that MI of possible non-paralyzed movements improved reach-to-grasp performance by: (i) increasing both tenodesis grasp capabilities and muscle strength; (ii) decreasing movement time (MT), and trajectory variability; and (iii) reducing the abnormally increased brain activity. MI can also strengthen motor commands by potentiating recruitment and synchronization of motoneurons, which leads to improved recovery. These improvements reflect brain adaptations induced by MI. Furthermore, MI can be used to control brain-computer interfaces (BCI) that successfully restore grasp capabilities. These results highlight the growing interest for MI and its potential to recover functional grasping in individuals with tetraplegia, and motivate the need for further studies to substantiate it.
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Affiliation(s)
- Sébastien Mateo
- ImpAct Team, Lyon Neuroscience Research Center, Université Lyon 1, Université de Lyon, INSERM U1028, CNRS UMR5292 Lyon, France ; Hospices Civils de Lyon, Hôpital Henry Gabrielle, Plateforme Mouvement et Handicap Lyon, France ; Centre de Recherche et d'Innovation sur le Sport, EA 647, Performance Motrice, Mentale et du Matériel, Université Lyon 1, Université de Lyon Villeurbanne, France ; Ecole Normale Supérieure de Lyon, CNRS UMR5672 Lyon, France
| | - Franck Di Rienzo
- Centre de Recherche et d'Innovation sur le Sport, EA 647, Performance Motrice, Mentale et du Matériel, Université Lyon 1, Université de Lyon Villeurbanne, France
| | - Vance Bergeron
- Ecole Normale Supérieure de Lyon, CNRS UMR5672 Lyon, France
| | - Aymeric Guillot
- Centre de Recherche et d'Innovation sur le Sport, EA 647, Performance Motrice, Mentale et du Matériel, Université Lyon 1, Université de Lyon Villeurbanne, France ; Institut Universitaire de France Paris, France
| | - Christian Collet
- Centre de Recherche et d'Innovation sur le Sport, EA 647, Performance Motrice, Mentale et du Matériel, Université Lyon 1, Université de Lyon Villeurbanne, France
| | - Gilles Rode
- ImpAct Team, Lyon Neuroscience Research Center, Université Lyon 1, Université de Lyon, INSERM U1028, CNRS UMR5292 Lyon, France ; Hospices Civils de Lyon, Hôpital Henry Gabrielle, Plateforme Mouvement et Handicap Lyon, France
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