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Dash D, Ferrari P, Wang J. Neural Decoding of Spontaneous Overt and Intended Speech. JOURNAL OF SPEECH, LANGUAGE, AND HEARING RESEARCH : JSLHR 2024:1-10. [PMID: 39106199 DOI: 10.1044/2024_jslhr-24-00046] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 08/09/2024]
Abstract
PURPOSE The aim of this study was to decode intended and overt speech from neuromagnetic signals while the participants performed spontaneous overt speech tasks without cues or prompts (stimuli). METHOD Magnetoencephalography (MEG), a noninvasive neuroimaging technique, was used to collect neural signals from seven healthy adult English speakers performing spontaneous, overt speech tasks. The participants randomly spoke the words yes or no at a self-paced rate without cues. Two machine learning models, namely, linear discriminant analysis (LDA) and one-dimensional convolutional neural network (1D CNN), were employed to classify the two words from the recorded MEG signals. RESULTS LDA and 1D CNN achieved average decoding accuracies of 79.02% and 90.40%, respectively, in decoding overt speech, significantly surpassing the chance level (50%). The accuracy for decoding intended speech was 67.19% using 1D CNN. CONCLUSIONS This study showcases the possibility of decoding spontaneous overt and intended speech directly from neural signals in the absence of perceptual interference. We believe that these findings make a steady step toward the future spontaneous speech-based brain-computer interface.
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Affiliation(s)
- Debadatta Dash
- Department of Neurology, The University of Texas at Austin
| | - Paul Ferrari
- Helen DeVos Children's Hospital, Corewell Health, Grand Rapids, MI
| | - Jun Wang
- Department of Neurology, The University of Texas at Austin
- Department of Speech, Language, and Hearing Sciences, The University of Texas at Austin
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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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Herbert C. Brain-computer interfaces and human factors: the role of language and cultural differences-Still a missing gap? Front Hum Neurosci 2024; 18:1305445. [PMID: 38665897 PMCID: PMC11043545 DOI: 10.3389/fnhum.2024.1305445] [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: 10/01/2023] [Accepted: 02/02/2024] [Indexed: 04/28/2024] Open
Abstract
Brain-computer interfaces (BCIs) aim at the non-invasive investigation of brain activity for supporting communication and interaction of the users with their environment by means of brain-machine assisted technologies. Despite technological progress and promising research aimed at understanding the influence of human factors on BCI effectiveness, some topics still remain unexplored. The aim of this article is to discuss why it is important to consider the language of the user, its embodied grounding in perception, action and emotions, and its interaction with cultural differences in information processing in future BCI research. Based on evidence from recent studies, it is proposed that detection of language abilities and language training are two main topics of enquiry of future BCI studies to extend communication among vulnerable and healthy BCI users from bench to bedside and real world applications. In addition, cultural differences shape perception, actions, cognition, language and emotions subjectively, behaviorally as well as neuronally. Therefore, BCI applications should consider cultural differences in information processing to develop culture- and language-sensitive BCI applications for different user groups and BCIs, and investigate the linguistic and cultural contexts in which the BCI will be used.
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Affiliation(s)
- Cornelia Herbert
- Applied Emotion and Motivation Psychology, Institute of Psychology and Education, Ulm University, Ulm, Germany
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4
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Shi X, Li B, Wang W, Qin Y, Wang H, Wang X. Classification Algorithm for Electroencephalogram-based Motor Imagery Using Hybrid Neural Network with Spatio-temporal Convolution and Multi-head Attention Mechanism. Neuroscience 2023; 527:64-73. [PMID: 37517788 DOI: 10.1016/j.neuroscience.2023.07.020] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/11/2023] [Revised: 07/11/2023] [Accepted: 07/16/2023] [Indexed: 08/01/2023]
Abstract
Motor imagery (MI) is a brain-computer interface (BCI) technique in which specific brain regions are activated when people imagine their limbs (or muscles) moving, even without actual movement. The technology converts electroencephalogram (EEG) signals generated by the brain into computer-readable commands by measuring neural activity. Classification of motor imagery is one of the tasks in BCI. Researchers have done a lot of work on motor imagery classification, and the existing literature has relatively mature decoding methods for two-class motor tasks. However, as the categories of EEG-based motor imagery tasks increase, further exploration is needed for decoding research on four-class motor imagery tasks. In this study, we designed a hybrid neural network that combines spatiotemporal convolution and attention mechanisms. Specifically, the data is first processed by spatiotemporal convolution to extract features and then processed by a Multi-branch Convolution block. Finally, the processed data is input into the encoder layer of the Transformer for a self-attention calculation to obtain the classification results. Our approach was tested on the well-known MI datasets BCI Competition IV 2a and 2b, and the results show that the 2a dataset has a global average classification accuracy of 83.3% and a kappa value of 0.78. Experimental results show that the proposed method outperforms most of the existing methods.
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Affiliation(s)
- Xingbin Shi
- The School of Electrical Engineering, Shanghai DianJi University, Shanghai, China; Intelligent Decision and Control Technology Institute, Shanghai Dianji University, Shanghai, China
| | - Baojiang Li
- The School of Electrical Engineering, Shanghai DianJi University, Shanghai, China; Intelligent Decision and Control Technology Institute, Shanghai Dianji University, Shanghai, China.
| | - Wenlong Wang
- The School of Electrical Engineering, Shanghai DianJi University, Shanghai, China; Intelligent Decision and Control Technology Institute, Shanghai Dianji University, Shanghai, China
| | - Yuxin Qin
- The School of Electrical Engineering, Shanghai DianJi University, Shanghai, China; Intelligent Decision and Control Technology Institute, Shanghai Dianji University, Shanghai, China
| | - Haiyan Wang
- The School of Electrical Engineering, Shanghai DianJi University, Shanghai, China; Intelligent Decision and Control Technology Institute, Shanghai Dianji University, Shanghai, China
| | - Xichao Wang
- The School of Electrical Engineering, Shanghai DianJi University, Shanghai, China; Intelligent Decision and Control Technology Institute, Shanghai Dianji University, Shanghai, China
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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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6
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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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Pan J, Chen X, Ban N, He J, Chen J, Huang H. Advances in P300 brain-computer interface spellers: toward paradigm design and performance evaluation. Front Hum Neurosci 2022; 16:1077717. [PMID: 36618996 PMCID: PMC9810759 DOI: 10.3389/fnhum.2022.1077717] [Citation(s) in RCA: 5] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/23/2022] [Accepted: 11/23/2022] [Indexed: 12/24/2022] Open
Abstract
A brain-computer interface (BCI) is a non-muscular communication technology that provides an information exchange channel for our brains and external devices. During the decades, BCI has made noticeable progress and has been applied in many fields. One of the most traditional BCI applications is the BCI speller. This article primarily discusses the progress of research into P300 BCI spellers and reviews four types of P300 spellers: single-modal P300 spellers, P300 spellers based on multiple brain patterns, P300 spellers with multisensory stimuli, and P300 spellers with multiple intelligent techniques. For each type of P300 speller, we further review several representative P300 spellers, including their design principles, paradigms, algorithms, experimental performance, and corresponding advantages. We particularly emphasized the paradigm design ideas, including the overall layout, individual symbol shapes and stimulus forms. Furthermore, several important issues and research guidance for the P300 speller were identified. We hope that this review can assist researchers in learning the new ideas of these novel P300 spellers and enhance their practical application capability.
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Affiliation(s)
- Jiahui Pan
- *Correspondence: Jiahui Pan Haiyun Huang
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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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Shen J, Tang S, Yan B, Xie D, Fang T, Chen L, Li G. Pain assessment during physiotherapy and noxious stimuli in patients with disorders of consciousness: A preliminary study. Front Integr Neurosci 2022; 16:962077. [PMID: 36159090 PMCID: PMC9492971 DOI: 10.3389/fnint.2022.962077] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/05/2022] [Accepted: 08/22/2022] [Indexed: 12/03/2022] Open
Abstract
Objectives The primary purpose of this study is to determine whether patients with disorders of consciousness (DOC) (unresponsive wakefulness syndrome, UWS; minimally conscious state, MCS) experience pain during physiotherapy and noxious stimuli in a larger patient population. Materials and methods The patients’ level of consciousness was measured with the Coma Recovery Scale-Revised (CRS-R). Additionally, the Nociception Coma Scale-revised (NCS-R) was used to assess their pain response. The NCS-R total scores between UWS and MCS at baseline, physiotherapy and noxious stimulus were compared using the Mann-Whitney U test (Wilcoxon rank-sum test) and the Kruskal-Wallis H test with Bonferroni correction. Results The study enrolled 93 participants. There was a statistically significant difference in NCS-R total scores between the three conditions (H = 215.25, p < 0.001). At baseline, there was no statistically significant difference between MCS and UWS (U = 378, z = –1.35, p = 0.178). While there was a statistically significant difference between MCS and UWS during physiotherapy (U = 1,362, z = –3.06, p < 0.01) and under noxious stimuli (U = 5142.5, z = –11.22, p < 0.001). Conclusion Physiotherapy improved the activity responsiveness of DOC patients, and patients experienced less potential pain. However, some DOC patients, especially MCS patients, perceived pain under the noxious stimuli.
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Pan J, Xiao J, Wang J, Wang F, Li J, Qiu L, Di H, Li Y. Brain-Computer Interfaces for Awareness Detection, Auxiliary Diagnosis, Prognosis and Rehabilitation in Patients with Disorders of Consciousness. Semin Neurol 2022; 42:363-374. [PMID: 35835448 DOI: 10.1055/a-1900-7261] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/17/2022]
Affiliation(s)
- Jiahui Pan
- Pazhou Lab, Guangzhou, China.,South China Normal University, Guangzhou, China
| | - Jun Xiao
- Pazhou Lab, Guangzhou, China.,South China University of Technology, Guangzhou, China
| | - Jing Wang
- Hangzhou Normal University, Hangzhou, China
| | - Fei Wang
- Pazhou Lab, Guangzhou, China.,South China Normal University, Guangzhou, China
| | - Jingcong Li
- Pazhou Lab, Guangzhou, China.,South China Normal University, Guangzhou, China
| | - Lina Qiu
- South China Normal University, Guangzhou, China
| | - Haibo Di
- Hangzhou Normal University, Hangzhou, China
| | - Yuanqing Li
- Pazhou Lab, Guangzhou, China.,South China University of Technology, Guangzhou, China
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Liu C, Jin J, Daly I, Li S, Sun H, Huang Y, Wang X, Cichocki A. SincNet-based Hybrid Neural Network for Motor Imagery EEG Decoding. IEEE Trans Neural Syst Rehabil Eng 2022; 30:540-549. [PMID: 35235515 DOI: 10.1109/tnsre.2022.3156076] [Citation(s) in RCA: 9] [Impact Index Per Article: 4.5] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/09/2022]
Abstract
It is difficult to identify optimal cut-off frequencies for filters used with the common spatial pattern (CSP) method in motor imagery (MI)-based brain-computer interfaces (BCIs). Most current studies choose filter cut-frequencies based on experience or intuition, resulting in sub-optimal use of MI-related spectral information in the electroencephalography (EEG). To improve information utilization, we propose a SincNet-based hybrid neural network (SHNN) for MI-based BCIs. First, raw EEG is segmented into different time windows and mapped into the CSP feature space. Then, SincNets are used as filter bank band-pass filters to automatically filter the data. Next, we used squeeze-and-excitation modules to learn a sparse representation of the filtered data. The resulting sparse data were fed into convolutional neural networks to learn deep feature representations. Finally, these deep features were fed into a gated recurrent unit module to seek sequential relations, and a fully connected layer was used for classification. We used the BCI competition IV datasets 2a and 2b to verify the effectiveness of our SHNN method. The mean classification accuracies (kappa values) of our SHNN method are 0.7426 (0.6648) on dataset 2a and 0.8349 (0.6697) on dataset 2b, respectively. The statistical test results demonstrate that our SHNN can significantly outperform other state-of-the-art methods on these datasets.
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A Systematic Review of Sleep in Patients with Disorders of Consciousness: From Diagnosis to Prognosis. Brain Sci 2021; 11:brainsci11081072. [PMID: 34439690 PMCID: PMC8393958 DOI: 10.3390/brainsci11081072] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/03/2021] [Revised: 08/02/2021] [Accepted: 08/13/2021] [Indexed: 10/26/2022] Open
Abstract
With the development of intensive care technology, the number of patients who survive acute severe brain injury has increased significantly. At present, it is difficult to diagnose the patients with disorders of consciousness (DOCs) because motor responses in these patients may be very limited and inconsistent. Electrophysiological criteria, such as event-related potentials or motor imagery, have also been studied to establish a diagnosis and prognosis based on command-following or active paradigms. However, the use of such task-based techniques in DOC patients is methodologically complex and requires careful analysis and interpretation. The present paper focuses on the analysis of sleep patterns for the evaluation of DOC and its relationships with diagnosis and prognosis outcomes. We discuss the concepts of sleep patterns in patients suffering from DOC, identification of this challenging population, and the prognostic value of sleep. The available literature on individuals in an unresponsive wakefulness syndrome (UWS) or minimally conscious state (MCS) following traumatic or nontraumatic severe brain injury is reviewed. We can distinguish patients with different levels of consciousness by studying sleep patients with DOC. Most MCS patients have sleep and wake alternations, sleep spindles and rapid eye movement (REM) sleep, while UWS patients have few EEG changes. A large number of sleep spindles and organized sleep-wake patterns predict better clinical outcomes. It is expected that this review will promote our understanding of sleep EEG in DOC.
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