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Yi Z, Pan J, Chen Z, Lu D, Cai H, Li J, Xie Q. A Hybrid BCI Integrating EEG and Eye-Tracking for Assisting Clinical Communication in Patients With Disorders of Consciousness. IEEE Trans Neural Syst Rehabil Eng 2024; 32:2759-2771. [PMID: 39074021 DOI: 10.1109/tnsre.2024.3435016] [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: 07/31/2024]
Abstract
Assessing communication abilities in patients with disorders of consciousness (DOCs) is challenging due to limitations in the behavioral scale. Electroencephalogram-based brain-computer interfaces (BCIs) and eye-tracking for detecting ocular changes can capture mental activities without requiring physical behaviors and thus may be a solution. This study proposes a hybrid BCI that integrates EEG and eye tracking to facilitate communication in patients with DOC. Specifically, the BCI presented a question and two randomly flashing answers (yes/no). The subjects were instructed to focus on an answer. A multimodal target recognition network (MTRN) is proposed to detect P300 potentials and eye-tracking responses (i.e., pupil constriction and gaze) and identify the target in real time. In the MTRN, the dual-stream feature extraction module with two independent multiscale convolutional neural networks extracts multiscale features from multimodal data. Then, the multimodal attention strategy adaptively extracts the most relevant information about the target from multimodal data. Finally, a prototype network is designed as a classifier to facilitate small-sample data classification. Ten healthy individuals, nine DOC patients and one LIS patient were included in this study. All healthy subjects achieved 100% accuracy. Five patients could communicate with our BCI, with 76.1±7.9% accuracy. Among them, two patients who were noncommunicative on the behavioral scale exhibited communication ability via our BCI. Additionally, we assessed the performance of unimodal BCIs and compared MTRNs with other methods. All the results suggested that our BCI can yield more sensitive outcomes than the CRS-R and can serve as a valuable communication tool.
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Chaudhary P, Dhankhar N, Singhal A, Rana KPS. A two-stage transformer based network for motor imagery classification. Med Eng Phys 2024; 128:104154. [PMID: 38697881 DOI: 10.1016/j.medengphy.2024.104154] [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: 06/22/2023] [Revised: 02/18/2024] [Accepted: 03/16/2024] [Indexed: 05/05/2024]
Abstract
Brain-computer interfaces (BCIs) are used to understand brain functioning and develop therapies for neurological and neurodegenerative disorders. Therefore, BCIs are crucial in rehabilitating motor dysfunction and advancing motor imagery applications. For motor imagery, electroencephalogram (EEG) signals are used to classify the subject's intention of moving a body part without actually moving it. This paper presents a two-stage transformer-based architecture that employs handcrafted features and deep learning techniques to enhance the classification performance on benchmarked EEG signals. Stage-1 is built on parallel convolution based EEGNet, multi-head attention, and separable temporal convolution networks for spatiotemporal feature extraction. Further, for enhanced classification, in stage-2, additional features and embeddings extracted from stage-1 are used to train TabNet. In addition, a novel channel cluster swapping data augmentation technique is also developed to handle the issue of limited samples for training deep learning architectures. The developed two-stage architecture offered an average classification accuracy of 88.5 % and 88.3 % on the BCI Competition IV-2a and IV-2b datasets, respectively, which is approximately 3.0 % superior over similar recent reported works.
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Affiliation(s)
- Priyanshu Chaudhary
- Department of Electronics and Communication Engineering, Netaji Subhas University of Technology, Delhi, India
| | - Nischay Dhankhar
- Department of Electronics and Communication Engineering, Netaji Subhas University of Technology, Delhi, India.
| | - Amit Singhal
- Department of Electronics and Communication Engineering, Netaji Subhas University of Technology, Delhi, India
| | - K P S Rana
- Department of Instrumentation and Control Engineering, Netaji Subhas University of Technology, Delhi, India
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Liu G, Chi B. Technological Modalities in the Assessment and Treatment of Disorders of Consciousness. Phys Med Rehabil Clin N Am 2024; 35:109-126. [PMID: 37993182 DOI: 10.1016/j.pmr.2023.07.005] [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] [Indexed: 11/24/2023]
Abstract
Over the last 10 years, there have been rapid advances made in technologies that can be utilized in the diagnosis and treatment of patients with a disorder of consciousness (DoC). This article provides a comprehensive review of these modalities including the evidence supporting their potential use in DoC. This review specifically addresses diagnostic, non-invasive therapeutic, and invasive therapeutic technological modalities except for neuroimaging, which is discussed in another article. While technologic advances appear promising for both assessment and treatment of patients with a DoC, high-quality evidence supporting widespread clinical adoption remains limited.
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Affiliation(s)
- Gang Liu
- Department of Rehabilitation Medicine, Huashan Hospital, Fudan University, No 12 Wulumuqi Middle Road, Shanghai 200040, China
| | - Bradley Chi
- H. Ben Taub Department of Physical Medicine and Rehabilitation, Baylor College of Medicine, 7200 Cambridge Street, Houston, TX 77030, USA.
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Chai X, Cao T, He Q, Wang N, Zhang X, Shan X, Lv Z, Tu W, Yang Y, Zhao J. Brain-computer interface digital prescription for neurological disorders. CNS Neurosci Ther 2024; 30:e14615. [PMID: 38358054 PMCID: PMC10867871 DOI: 10.1111/cns.14615] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/17/2023] [Revised: 12/13/2023] [Accepted: 01/09/2024] [Indexed: 02/16/2024] Open
Abstract
Neurological and psychiatric diseases can lead to motor, language, emotional disorder, and cognitive, hearing or visual impairment By decoding the intention of the brain in real time, the Brain-computer interface (BCI) can first assist in the diagnosis of diseases, and can also compensate for its damaged function by directly interacting with the environment; In addition, provide output signals in various forms, such as actual motion, tactile or visual feedback, to assist in rehabilitation training; Further intervention in brain disorders is achieved by close-looped neural modulation. In this article, we envision the future BCI digital prescription system for patients with different functional disorders and discuss the key contents in the prescription the brain signals, coding and decoding protocols and interaction paradigms, and assistive technology. Then, we discuss the details that need to be specially included in the digital prescription for different intervention technologies. The third part summarizes previous examples of intervention, focusing on how to select appropriate interaction paradigms for patients with different functional impairments. For the last part, we discussed the indicators and influencing factors in evaluating the therapeutic effect of BCI as intervention.
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Affiliation(s)
- Xiaoke Chai
- Brain Computer Interface Transitional Research Center, Beijing Tiantan HospitalCapital Medical UniversityBeijingChina
- China National Center for Neurological DisordersBeijingChina
- Translation Laboratory of Clinical MedicineChinese Institute for Brain Research & Beijing Tiantan HospitalBeijingChina
| | - Tianqing Cao
- Department of Neurosurgery, Beijing Tiantan HospitalCapital Medical UniversityBeijingChina
- China National Clinical Research Center for Neurological DiseasesBeijingChina
| | - Qiheng He
- Department of Neurosurgery, Beijing Tiantan HospitalCapital Medical UniversityBeijingChina
- China National Clinical Research Center for Neurological DiseasesBeijingChina
| | - Nan Wang
- Department of Neurosurgery, Beijing Tiantan HospitalCapital Medical UniversityBeijingChina
- China National Clinical Research Center for Neurological DiseasesBeijingChina
| | - Xuemin Zhang
- National Research Center for Rehabilitation Technical AidsBeijingChina
| | - Xinying Shan
- National Research Center for Rehabilitation Technical AidsBeijingChina
| | - Zeping Lv
- National Research Center for Rehabilitation Technical AidsBeijingChina
| | - Wenjun Tu
- Translation Laboratory of Clinical MedicineChinese Institute for Brain Research & Beijing Tiantan HospitalBeijingChina
- Department of Neurosurgery, Beijing Tiantan HospitalCapital Medical UniversityBeijingChina
| | - Yi Yang
- Brain Computer Interface Transitional Research Center, Beijing Tiantan HospitalCapital Medical UniversityBeijingChina
- China National Center for Neurological DisordersBeijingChina
- Translation Laboratory of Clinical MedicineChinese Institute for Brain Research & Beijing Tiantan HospitalBeijingChina
- Department of Neurosurgery, Beijing Tiantan HospitalCapital Medical UniversityBeijingChina
- China National Clinical Research Center for Neurological DiseasesBeijingChina
- National Research Center for Rehabilitation Technical AidsBeijingChina
- Beijing Institute of Brain DisordersBeijingChina
- Chinese Institute for Brain ResearchBeijingChina
| | - Jizong Zhao
- Brain Computer Interface Transitional Research Center, Beijing Tiantan HospitalCapital Medical UniversityBeijingChina
- China National Center for Neurological DisordersBeijingChina
- Translation Laboratory of Clinical MedicineChinese Institute for Brain Research & Beijing Tiantan HospitalBeijingChina
- Department of Neurosurgery, Beijing Tiantan HospitalCapital Medical UniversityBeijingChina
- China National Clinical Research Center for Neurological DiseasesBeijingChina
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Pan J, Liang R, He Z, Li J, Liang Y, Zhou X, He Y, Li Y. ST-SCGNN: A Spatio-Temporal Self-Constructing Graph Neural Network for Cross-Subject EEG-Based Emotion Recognition and Consciousness Detection. IEEE J Biomed Health Inform 2024; 28:777-788. [PMID: 38015677 DOI: 10.1109/jbhi.2023.3335854] [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: 11/30/2023]
Abstract
In this paper, a novel spatio-temporal self-constructing graph neural network (ST-SCGNN) is proposed for cross-subject emotion recognition and consciousness detection. For spatio-temporal feature generation, activation and connection pattern features are first extracted and then combined to leverage their complementary emotion-related information. Next, a self-constructing graph neural network with a spatio-temporal model is presented. Specifically, the graph structure of the neural network is dynamically updated by the self-constructing module of the input signal. Experiments based on the SEED and SEED-IV datasets showed that the model achieved average accuracies of 85.90% and 76.37%, respectively. Both values exceed the state-of-the-art metrics with the same protocol. In clinical besides, patients with disorders of consciousness (DOC) suffer severe brain injuries, and sufficient training data for EEG-based emotion recognition cannot be collected. Our proposed ST-SCGNN method for cross-subject emotion recognition was first attempted in training in ten healthy subjects and testing in eight patients with DOC. We found that two patients obtained accuracies significantly higher than chance level and showed similar neural patterns with healthy subjects. Covert consciousness and emotion-related abilities were thus demonstrated in these two patients. Our proposed ST-SCGNN for cross-subject emotion recognition could be a promising tool for consciousness detection in DOC patients.
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Vatrano M, Nemirovsky IE, Tonin P, Riganello F. Assessing Consciousness through Neurofeedback and Neuromodulation: Possibilities and Challenges. Life (Basel) 2023; 13:1675. [PMID: 37629532 PMCID: PMC10455583 DOI: 10.3390/life13081675] [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: 07/03/2023] [Revised: 07/27/2023] [Accepted: 07/30/2023] [Indexed: 08/27/2023] Open
Abstract
Neurofeedback is a non-invasive therapeutic approach that has gained traction in recent years, showing promising results for various neurological and psychiatric conditions. It involves real-time monitoring of brain activity, allowing individuals to gain control over their own brainwaves and improve cognitive performance or alleviate symptoms. The use of electroencephalography (EEG), such as brain-computer interface (BCI), transcranial direct current stimulation (tDCS), and transcranial magnetic stimulation (TMS), has been instrumental in developing neurofeedback techniques. However, the application of these tools in patients with disorders of consciousness (DoC) presents unique challenges. In this narrative review, we explore the use of neurofeedback in treating patients with DoC. More specifically, we discuss the advantages and challenges of using tools such as EEG neurofeedback, tDCS, TMS, and BCI for these conditions. Ultimately, we hope to provide the neuroscientific community with a comprehensive overview of neurofeedback and emphasize its potential therapeutic applications in severe cases of impaired consciousness levels.
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Affiliation(s)
- Martina Vatrano
- S. Anna Institute, Research in Advanced Neurorehabilitation, Via Siris, 11, 88900 Crotone, Italy;
| | - Idan Efim Nemirovsky
- Department of Physics and Astronomy, Brain and Mind Institute, University of Western Ontario, London, ON N6A 3K7, Canada;
| | - Paolo Tonin
- S. Anna Institute, Research in Advanced Neurorehabilitation, Via Siris, 11, 88900 Crotone, Italy;
| | - Francesco Riganello
- S. Anna Institute, Research in Advanced Neurorehabilitation, Via Siris, 11, 88900 Crotone, Italy;
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Wang F, Wan Y, Li Z, Qi F, Li J. A cross-subject decoding algorithm for patients with disorder of consciousness based on P300 brain computer interface. Front Neurosci 2023; 17:1167125. [PMID: 37547152 PMCID: PMC10398338 DOI: 10.3389/fnins.2023.1167125] [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: 02/16/2023] [Accepted: 06/19/2023] [Indexed: 08/08/2023] Open
Abstract
Background Brain computer interface (BCI) technology may provide a new way of communication for some patients with disorder of consciousness (DOC), which can directly connect the brain and external devices. However, the DOC patients' EEG differ significantly from that of the normal person and are difficult to collected, the decoding algorithm currently only is trained based on a small amount of the patient's own data and performs poorly. Methods In this study, a decoding algorithm called WD-ADSTCN based on domain adaptation is proposed to improve the DOC patients' P300 signal detection. We used the Wasserstein distance to filter the normal population data to increase the training data. Furthermore, an adversarial approach is adopted to resolve the differences between the normal and patient data. Results The results showed that in the cross-subject P300 detection of DOC patients, 7 of 11 patients achieved an average accuracy of over 70%. Furthermore, their clinical diagnosis changed and CRS-R scores improved three months after the experiment. Conclusion These results demonstrated that the proposed method could be employed in the P300 BCI system for the DOC patients, which has important implications for the clinical diagnosis and prognosis of these patients.
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Affiliation(s)
- Fei Wang
- School of Software, South China Normal University, Guangzhou, China
- Pazhou Lab, Guangzhou, China
| | - Yinxing Wan
- School of Software, South China Normal University, Guangzhou, China
| | - Zhuorong Li
- School of Software, South China Normal University, Guangzhou, China
| | - Feifei Qi
- Pazhou Lab, Guangzhou, China
- School of Internet Finance and Information Engineering, Guangdong University of Finance, Guangzhou, China
| | - Jingcong Li
- School of Software, South China Normal University, Guangzhou, China
- Pazhou Lab, Guangzhou, China
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Galiotta V, Quattrociocchi I, D'Ippolito M, Schettini F, Aricò P, Sdoia S, Formisano R, Cincotti F, Mattia D, Riccio A. EEG-based Brain-Computer Interfaces for people with Disorders of Consciousness: Features and applications. A systematic review. Front Hum Neurosci 2022; 16:1040816. [PMID: 36545350 PMCID: PMC9760911 DOI: 10.3389/fnhum.2022.1040816] [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: 09/09/2022] [Accepted: 11/17/2022] [Indexed: 12/11/2022] Open
Abstract
Background Disorders of Consciousness (DoC) are clinical conditions following a severe acquired brain injury (ABI) characterized by absent or reduced awareness, known as coma, Vegetative State (VS)/Unresponsive Wakefulness Syndrome (VS/UWS), and Minimally Conscious State (MCS). Misdiagnosis rate between VS/UWS and MCS is attested around 40% due to the clinical and behavioral fluctuations of the patients during bedside consciousness assessments. Given the large body of evidence that some patients with DoC possess "covert" awareness, revealed by neuroimaging and neurophysiological techniques, they are candidates for intervention with brain-computer interfaces (BCIs). Objectives The aims of the present work are (i) to describe the characteristics of BCI systems based on electroencephalography (EEG) performed on DoC patients, in terms of control signals adopted to control the system, characteristics of the paradigm implemented, classification algorithms and applications (ii) to evaluate the performance of DoC patients with BCI. Methods The search was conducted on Pubmed, Web of Science, Scopus and Google Scholar. The PRISMA guidelines were followed in order to collect papers published in english, testing a BCI and including at least one DoC patient. Results Among the 527 papers identified with the first run of the search, 27 papers were included in the systematic review. Characteristics of the sample of participants, behavioral assessment, control signals employed to control the BCI, the classification algorithms, the characteristics of the paradigm, the applications and performance of BCI were the data extracted from the study. Control signals employed to operate the BCI were: P300 (N = 19), P300 and Steady-State Visual Evoked Potentials (SSVEP; hybrid system, N = 4), sensorimotor rhythms (SMRs; N = 5) and brain rhythms elicited by an emotional task (N = 1), while assessment, communication, prognosis, and rehabilitation were the possible applications of BCI in DoC patients. Conclusion Despite the BCI is a promising tool in the management of DoC patients, supporting diagnosis and prognosis evaluation, results are still preliminary, and no definitive conclusions may be drawn; even though neurophysiological methods, such as BCI, are more sensitive to covert cognition, it is suggested to adopt a multimodal approach and a repeated assessment strategy.
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Affiliation(s)
- Valentina Galiotta
- Neuroelectric Imaging and Brain-Computer Interface Laboratory, Fondazione Santa Lucia (IRCCS), Rome, Italy,Department of Psychology, Sapienza University of Rome, Rome, Italy
| | - Ilaria Quattrociocchi
- Neuroelectric Imaging and Brain-Computer Interface Laboratory, Fondazione Santa Lucia (IRCCS), Rome, Italy,Department of Computer, Control, and Management Engineering “Antonio Ruberti”, Sapienza University of Rome, Rome, Italy
| | - Mariagrazia D'Ippolito
- Neuroelectric Imaging and Brain-Computer Interface Laboratory, Fondazione Santa Lucia (IRCCS), Rome, Italy,*Correspondence: Mariagrazia D'Ippolito
| | - Francesca Schettini
- Neuroelectric Imaging and Brain-Computer Interface Laboratory, Fondazione Santa Lucia (IRCCS), Rome, Italy,Servizio di Ausilioteca per la Riabilitazione Assistita con Tecnologia, Fondazione Santa Lucia (IRCCS), Rome, Italy
| | - Pietro Aricò
- Department of Computer, Control, and Management Engineering “Antonio Ruberti”, Sapienza University of Rome, Rome, Italy,Department of Molecular Medicine, Sapienza University of Rome, Rome, Italy,BrainSigns srl, Rome, Italy
| | - Stefano Sdoia
- Department of Psychology, Sapienza University of Rome, Rome, Italy
| | - Rita Formisano
- Neurorehabilitation 2 and Post-Coma Unit, Fondazione Santa Lucia (IRCCS), Rome, Italy
| | - Febo Cincotti
- Department of Computer, Control, and Management Engineering “Antonio Ruberti”, Sapienza University of Rome, Rome, Italy
| | - Donatella Mattia
- Neuroelectric Imaging and Brain-Computer Interface Laboratory, Fondazione Santa Lucia (IRCCS), Rome, Italy,Servizio di Ausilioteca per la Riabilitazione Assistita con Tecnologia, Fondazione Santa Lucia (IRCCS), Rome, Italy
| | - Angela Riccio
- Neuroelectric Imaging and Brain-Computer Interface Laboratory, Fondazione Santa Lucia (IRCCS), Rome, Italy,Servizio di Ausilioteca per la Riabilitazione Assistita con Tecnologia, Fondazione Santa Lucia (IRCCS), Rome, Italy
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