{"doi":"10.3389/fnhum.2022.1040816","title":"EEG-based Brain-Computer Interfaces for people with Disorders of Consciousness: Features and applications. A systematic review","abstract":"<jats:sec><jats:title>Background</jats:title><jats:p>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).</jats:p></jats:sec><jats:sec><jats:title>Objectives</jats:title><jats:p>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.</jats:p></jats:sec><jats:sec><jats:title>Methods</jats:title><jats:p>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.</jats:p></jats:sec><jats:sec><jats:title>Results</jats:title><jats:p>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 (<jats:italic>N</jats:italic> = 19), P300 and Steady-State Visual Evoked Potentials (SSVEP; hybrid system, <jats:italic>N</jats:italic> = 4), sensorimotor rhythms (SMRs; <jats:italic>N</jats:italic> = 5) and brain rhythms elicited by an emotional task (<jats:italic>N</jats:italic> = 1), while assessment, communication, prognosis, and rehabilitation were the possible applications of BCI in DoC patients.</jats:p></jats:sec><jats:sec><jats:title>Conclusion</jats:title><jats:p>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.</jats:p></jats:sec>","journal":"Frontiers in Human Neuroscience","year":2022,"id":649491,"datarank":0.5333022092234121,"base_score":3.5553480614894135,"endowment":3.5553480614894135,"self_citation_contribution":0.5333022092234121,"citation_network_contribution":0.0,"self_endowment_contribution":0.5333022092234121,"citer_contribution":0.0,"corpus_percentile":null,"corpus_rank":null,"citation_count":34,"citer_count":0,"citers_with_citation_signal":0,"citers_with_endowment":0,"datacite_reuse_total":0,"is_dataset":false,"is_dataset_confidence":null,"is_data_producer":false,"deposit_databanks":null,"is_oa":false,"file_count":0,"downloads":0,"has_version_chain":false,"published_date":null,"fair_score":null,"fair_percentile":null,"algorithm_id":"datarank_citation_only_1hop_v6","ranking_scope":"data_only","authors":[{"id":1693162,"name":"Ilaria Quattrociocchi","orcid":null,"position":1,"is_corresponding":false},{"id":1693163,"name":"Mariagrazia D'Ippolito","orcid":null,"position":2,"is_corresponding":false},{"id":1693164,"name":"Francesca Schettini","orcid":null,"position":3,"is_corresponding":false},{"id":1693165,"name":"Pietro Aricò","orcid":null,"position":4,"is_corresponding":false},{"id":1693167,"name":"Stefano Sdoia","orcid":null,"position":5,"is_corresponding":false},{"id":278353,"name":"Rita Formisano","orcid":"0000-0002-2516-0540","position":6,"is_corresponding":false},{"id":706537,"name":"Febo Cincotti","orcid":"0000-0003-1898-6480","position":7,"is_corresponding":false},{"id":278354,"name":"Donatella Mattia","orcid":"0000-0002-3092-2511","position":8,"is_corresponding":false},{"id":1693171,"name":"Angela Riccio","orcid":null,"position":9,"is_corresponding":false},{"id":1693161,"name":"Valentina Galiotta","orcid":null,"position":0,"is_corresponding":false}],"reference_count":0,"raw_metadata":{"has_enrichment":true,"resolved":true,"title":"EEG-based Brain-Computer Interfaces for people with Disorders of Consciousness: Features and applications. A systematic review","abstract":"<jats:sec><jats:title>Background</jats:title><jats:p>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).</jats:p></jats:sec><jats:sec><jats:title>Objectives</jats:title><jats:p>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.</jats:p></jats:sec><jats:sec><jats:title>Methods</jats:title><jats:p>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.</jats:p></jats:sec><jats:sec><jats:title>Results</jats:title><jats:p>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 (<jats:italic>N</jats:italic> = 19), P300 and Steady-State Visual Evoked Potentials (SSVEP; hybrid system, <jats:italic>N</jats:italic> = 4), sensorimotor rhythms (SMRs; <jats:italic>N</jats:italic> = 5) and brain rhythms elicited by an emotional task (<jats:italic>N</jats:italic> = 1), while assessment, communication, prognosis, and rehabilitation were the possible applications of BCI in DoC patients.</jats:p></jats:sec><jats:sec><jats:title>Conclusion</jats:title><jats:p>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.</jats:p></jats:sec>","is_dataset_classified":null,"base_score":3.5553480614894135,"endowment":3.5553480614894135,"datacite_reuse_total":0,"file_count":0,"downloads":0,"views":0,"has_version_chain":false,"is_dataset":false,"is_oa":false,"pmid":"36545350","pmcid":"PMC9760911","openalex_id":"https://openalex.org/W4310675990","authors":[],"funders":[{"funder_name":"European Commission","grant_id":"778234","title":"Disorders of Consciousness (DoC): enhancing the transfer of knowledge and professional skills on evidence-based interventions and validated technology for a better management of patients"}],"total_grants":1,"fwci":1.2029,"citation_percentile":0.77791116,"influential_citations":0,"citation_trend":[{"year":2023,"count":6},{"year":2024,"count":8},{"year":2025,"count":12},{"year":2026,"count":8}],"oa_status":"gold","license":"cc-by","oa_locations":[{"url":"https://doi.org/10.3389/fnhum.2022.1040816","host_type":"journal"},{"url":"https://doi.org/10.3389/fnhum.2022.1040816","host_type":"publisher"},{"url":"https://www.frontiersin.org/articles/10.3389/fnhum.2022.1040816/full","host_type":"publisher"},{"url":"https://pubmed.ncbi.nlm.nih.gov/36545350","host_type":"repository"},{"url":"https://hdl.handle.net/11573/1664851","host_type":"repository"},{"url":"https://zenodo.org/record/7640028","host_type":"repository"},{"url":"https://doaj.org/article/75d8997a53a248b38fd2941b125bdbee","host_type":"repository"},{"url":"https://www.ncbi.nlm.nih.gov/pmc/articles/9760911","host_type":"repository"},{"url":"https://iris.uniroma1.it/bitstream/11573/1664851/1/Galiotta_EEG-based_2022.pdf","host_type":"repository"},{"url":"https://europepmc.org/articles/PMC9760911","host_type":"Europe_PMC"},{"url":"https://europepmc.org/articles/PMC9760911?pdf=render","host_type":"Europe_PMC"},{"url":"http://dx.doi.org/10.3389/fnhum.2022.1040816","host_type":""},{"url":"https://zenodo.org/records/7640028","host_type":""}],"fields_of_study":["EEG and Brain-Computer Interfaces","Traumatic Brain Injury Research","Functional Brain Connectivity Studies","03 medical and health sciences","0302 clinical medicine"],"mesh_terms":[],"keywords":["Brain–computer interface","Minimally conscious state","Persistent vegetative state","Electroencephalography","Wakefulness","Covert","Consciousness Disorders","Coma (optics)","Consciousness","Computer science","Psychology","Neuroimaging","Artificial intelligence","Physical medicine and rehabilitation","Neuroscience","Medicine","EEG","P300","Minimally Conscious State (Mcs)","Vegetative State (Vs)","Brain-computer Interface (Bci)","Disorders Of Consciousness (Doc)","Unresponsive Wakefulness Syndrome (Uws)","Cognitive Motor Dissociation","EEG; P300; Unresponsive Wakefulness Syndrome (UWS); brain-computer interface (BCI); cognitive motor dissociation; disorders of consciousness (DoC); minimally conscious state (MCS); vegetative state (VS)","Neurosciences. 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