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Kumar S, Alawieh H, Racz FS, Fakhreddine R, Millán JDR. Transfer learning promotes acquisition of individual BCI skills. PNAS NEXUS 2024; 3:pgae076. [PMID: 38426121 PMCID: PMC10903645 DOI: 10.1093/pnasnexus/pgae076] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 05/05/2023] [Accepted: 02/05/2024] [Indexed: 03/02/2024]
Abstract
Subject training is crucial for acquiring brain-computer interface (BCI) control. Typically, this requires collecting user-specific calibration data due to high inter-subject neural variability that limits the usability of generic decoders. However, calibration is cumbersome and may produce inadequate data for building decoders, especially with naïve subjects. Here, we show that a decoder trained on the data of a single expert is readily transferrable to inexperienced users via domain adaptation techniques allowing calibration-free BCI training. We introduce two real-time frameworks, (i) Generic Recentering (GR) through unsupervised adaptation and (ii) Personally Assisted Recentering (PAR) that extends GR by employing supervised recalibration of the decoder parameters. We evaluated our frameworks on 18 healthy naïve subjects over five online sessions, who operated a customary synchronous bar task with continuous feedback and a more challenging car racing game with asynchronous control and discrete feedback. We show that along with improved task-oriented BCI performance in both tasks, our frameworks promoted subjects' ability to acquire individual BCI skills, as the initial neurophysiological control features of an expert subject evolved and became subject specific. Furthermore, those features were task-specific and were learned in parallel as participants practiced the two tasks in every session. Contrary to previous findings implying that supervised methods lead to improved online BCI control, we observed that longitudinal training coupled with unsupervised domain matching (GR) achieved similar performance to supervised recalibration (PAR). Therefore, our presented frameworks facilitate calibration-free BCIs and have immediate implications for broader populations-such as patients with neurological pathologies-who might struggle to provide suitable initial calibration data.
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Affiliation(s)
- Satyam Kumar
- Chandra Family Department of Electrical and Computer Engineering, The University of Texas at Austin, Austin, TX 78712, USA
| | - Hussein Alawieh
- Chandra Family Department of Electrical and Computer Engineering, The University of Texas at Austin, Austin, TX 78712, USA
| | - Frigyes Samuel Racz
- Department of Neurology, The University of Texas at Austin, Austin, TX 78712, USA
- Mulva Clinic for the Neurosciences, The University of Texas at Austin, Austin, TX 78712, USA
| | - Rawan Fakhreddine
- Chandra Family Department of Electrical and Computer Engineering, The University of Texas at Austin, Austin, TX 78712, USA
| | - José del R Millán
- Chandra Family Department of Electrical and Computer Engineering, The University of Texas at Austin, Austin, TX 78712, USA
- Department of Neurology, The University of Texas at Austin, Austin, TX 78712, USA
- Mulva Clinic for the Neurosciences, The University of Texas at Austin, Austin, TX 78712, USA
- Departement of Biomedical Engineering, The University of Texas at Austin, Austin, TX 78712, USA
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Nagarajan A, Robinson N, Ang KK, Chua KSG, Chew E, Guan C. Transferring a deep learning model from healthy subjects to stroke patients in a motor imagery brain-computer interface. J Neural Eng 2024; 21:016007. [PMID: 38091617 DOI: 10.1088/1741-2552/ad152f] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/19/2023] [Accepted: 12/13/2023] [Indexed: 01/18/2024]
Abstract
Objective.Motor imagery (MI) brain-computer interfaces (BCIs) based on electroencephalogram (EEG) have been developed primarily for stroke rehabilitation, however, due to limited stroke data, current deep learning methods for cross-subject classification rely on healthy data. This study aims to assess the feasibility of applying MI-BCI models pre-trained using data from healthy individuals to detect MI in stroke patients.Approach.We introduce a new transfer learning approach where features from two-class MI data of healthy individuals are used to detect MI in stroke patients. We compare the results of the proposed method with those obtained from analyses within stroke data. Experiments were conducted using Deep ConvNet and state-of-the-art subject-specific machine learning MI classifiers, evaluated on OpenBMI two-class MI-EEG data from healthy subjects and two-class MI versus rest data from stroke patients.Main results.Results of our study indicate that through domain adaptation of a model pre-trained using healthy subjects' data, an average MI detection accuracy of 71.15% (±12.46%) can be achieved across 71 stroke patients. We demonstrate that the accuracy of the pre-trained model increased by 18.15% after transfer learning (p<0.001). Additionally, the proposed transfer learning method outperforms the subject-specific results achieved by Deep ConvNet and FBCSP, with significant enhancements of 7.64% (p<0.001) and 5.55% (p<0.001) in performance, respectively. Notably, the healthy-to-stroke transfer learning approach achieved similar performance to stroke-to-stroke transfer learning, with no significant difference (p>0.05). Explainable AI analyses using transfer models determined channel relevance patterns that indicate contributions from the bilateral motor, frontal, and parietal regions of the cortex towards MI detection in stroke patients.Significance.Transfer learning from healthy to stroke can enhance the clinical use of BCI algorithms by overcoming the challenge of insufficient clinical data for optimal training.
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Affiliation(s)
- Aarthy Nagarajan
- School of Computer Science and Engineering, Nanyang Technological University, 50 Nanyang Ave, Singapore 639798, Singapore
| | - Neethu Robinson
- School of Computer Science and Engineering, Nanyang Technological University, 50 Nanyang Ave, Singapore 639798, Singapore
| | - Kai Keng Ang
- School of Computer Science and Engineering, Nanyang Technological University, 50 Nanyang Ave, Singapore 639798, Singapore
- Institute for Infocomm Research, Agency of Science, Technology and Research (A*STAR), 1 Fusionopolis Way, Singapore 138632, Singapore
| | - Karen Sui Geok Chua
- Department of Rehabilitation Medicine, Tan Tock Seng Hospital, 11 Jln Tan Tock Seng, Singapore 308433, Singapore
| | - Effie Chew
- National University Health System, 1E Kent Ridge Road, Singapore 119228, Singapore
| | - Cuntai Guan
- School of Computer Science and Engineering, Nanyang Technological University, 50 Nanyang Ave, Singapore 639798, Singapore
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Ivanov N, Lio A, Chau T. Towards user-centric BCI design: Markov chain-based user assessment for mental imagery EEG-BCIs. J Neural Eng 2023; 20:066037. [PMID: 38128128 DOI: 10.1088/1741-2552/ad17f2] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/15/2023] [Accepted: 12/21/2023] [Indexed: 12/23/2023]
Abstract
Objective.While electroencephalography (EEG)-based brain-computer interfaces (BCIs) have many potential clinical applications, their use is impeded by poor performance for many users. To improve BCI performance, either via enhanced signal processing or user training, it is critical to understand and describe each user's ability to perform mental control tasks and produce discernible EEG patterns. While classification accuracy has predominantly been used to assess user performance, limitations and criticisms of this approach have emerged, thus prompting the need to develop novel user assessment approaches with greater descriptive capability. Here, we propose a combination of unsupervised clustering and Markov chain models to assess and describe user skill.Approach.Using unsupervisedK-means clustering, we segmented the EEG signal space into regions representing pattern states that users could produce. A user's movement through these pattern states while performing different tasks was modeled using Markov chains. Finally, using the steady-state distributions and entropy rates of the Markov chains, we proposed two metricstaskDistinctandrelativeTaskInconsistencyto assess, respectively, a user's ability to (i) produce distinct task-specific patterns for each mental task and (ii) maintain consistent patterns during individual tasks.Main results.Analysis of data from 14 adolescents using a three-class BCI revealed significant correlations between thetaskDistinctandrelativeTaskInconsistencymetrics and classification F1 score. Moreover, analysis of the pattern states and Markov chain models yielded descriptive information regarding user performance not immediately apparent from classification accuracy.Significance.Our proposed user assessment method can be used in concert with classifier-based analysis to further understand the extent to which users produce task-specific, time-evolving EEG patterns. In turn, this information could be used to enhance user training or classifier design.
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Affiliation(s)
- Nicolas Ivanov
- Bloorview Research Institute, Holland Bloorview Kids Rehabilitation Hospital, Toronto, ON, Canada
- Institute of Biomedical Engineering, University of Toronto, Toronto, ON, Canada
| | - Aaron Lio
- Division of Engineering Science, University of Toronto, Toronto, ON, Canada
| | - Tom Chau
- Bloorview Research Institute, Holland Bloorview Kids Rehabilitation Hospital, Toronto, ON, Canada
- Institute of Biomedical Engineering, University of Toronto, Toronto, ON, Canada
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Tonin L, Perdikis S, Kuzu TD, Pardo J, Orset B, Lee K, Aach M, Schildhauer TA, Martínez-Olivera R, Millán JDR. Learning to control a BMI-driven wheelchair for people with severe tetraplegia. iScience 2022; 25:105418. [PMID: 36590466 PMCID: PMC9801246 DOI: 10.1016/j.isci.2022.105418] [Citation(s) in RCA: 4] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/25/2022] [Revised: 08/14/2022] [Accepted: 10/18/2022] [Indexed: 11/19/2022] Open
Abstract
Mind-controlled wheelchairs are an intriguing assistive mobility solution applicable in complete paralysis. Despite progress in brain-machine interface (BMI) technology, its translation remains elusive. The primary objective of this study is to probe the hypothesis that BMI skill acquisition by end-users is fundamental to control a non-invasive brain-actuated intelligent wheelchair in real-world settings. We demonstrate that three tetraplegic spinal-cord injury users could be trained to operate a non-invasive, self-paced thought-controlled wheelchair and execute complex navigation tasks. However, only the two users exhibiting increasing decoding performance and feature discriminancy, significant neuroplasticity changes and improved BMI command latency, achieved high navigation performance. In addition, we show that dexterous, continuous control of robots is possible through low-degree of freedom, discrete and uncertain control channels like a motor imagery BMI, by blending human and artificial intelligence through shared-control methodologies. We posit that subject learning and shared-control are the key components paving the way for translational non-invasive BMI.
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Affiliation(s)
- Luca Tonin
- Department of Information Engineering, University of Padova, Padova, Italy,Padova Neuroscience Center, University of Padova, Padova, Italy
| | - Serafeim Perdikis
- Brain-Computer Interfaces and Neural Engineering Laboratory, School of Computer Science and Electronic Engineering, University of Essex, Colchester, UK
| | - Taylan Deniz Kuzu
- Klinik für Neurochirurgie und Wirbelsäulenchirurgie, Universitätsklinikum Bergmannsheil Bochum, Ruhr-Universität Bochum, Bochum, Germany
| | - Jorge Pardo
- Klinik für Neurochirurgie und Wirbelsäulenchirurgie, Universitätsklinikum Bergmannsheil Bochum, Ruhr-Universität Bochum, Bochum, Germany
| | - Bastien Orset
- École Polytechnique Fédérale de Lausanne, Geneva, Switzerland
| | - Kyuhwa Lee
- Wyss Center for Bio and Neuroengineering, Geneva, Switzerland
| | - Mirko Aach
- Chirurgische Universitätsklinik und Poliklinik, Universitätsklinikum Bergmannsheil Bochum, Ruhr-Universität Bochum, Bochum, Germany
| | - Thomas Armin Schildhauer
- Chirurgische Universitätsklinik und Poliklinik, Universitätsklinikum Bergmannsheil Bochum, Ruhr-Universität Bochum, Bochum, Germany
| | - Ramón Martínez-Olivera
- Klinik für Neurochirurgie und Wirbelsäulenchirurgie, Universitätsklinikum Bergmannsheil Bochum, Ruhr-Universität Bochum, Bochum, Germany
| | - José del R. Millán
- Department of Electrical and Computer Engineering, The University of Texas at Austin, Austin, TX, USA,Department of Neurology, The University of Texas at Austin, Austin, TX, USA,Corresponding author
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