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Rosenfelder MJ, Spiliopoulou M, Hoppenstedt B, Pryss R, Fissler P, della Piedra Walter M, Kolassa IT, Bender A. Stability of mental motor-imagery classification in EEG depends on the choice of classifier model and experiment design, but not on signal preprocessing. Front Comput Neurosci 2023; 17:1142948. [PMID: 37180880 PMCID: PMC10169631 DOI: 10.3389/fncom.2023.1142948] [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: 01/12/2023] [Accepted: 04/05/2023] [Indexed: 05/16/2023] Open
Abstract
Introduction Modern consciousness research has developed diagnostic tests to improve the diagnostic accuracy of different states of consciousness via electroencephalography (EEG)-based mental motor imagery (MI), which is still challenging and lacks a consensus on how to best analyse MI EEG-data. An optimally designed and analyzed paradigm must detect command-following in all healthy individuals, before it can be applied in patients, e.g., for the diagnosis of disorders of consciousness (DOC). Methods We investigated the effects of two important steps in the raw signal preprocessing on predicting participant performance (F1) and machine-learning classifier performance (area-under-curve, AUC) in eight healthy individuals, that are based solely on MI using high-density EEG (HD-EEG): artifact correction (manual correction with vs. without Independent Component Analysis [ICA]), region of interest (ROI; motor area vs. whole brain), and machine-learning algorithm (support-vector machine [SVM] vs. k-nearest neighbor [KNN]). Results Results revealed no significant effects of artifact correction and ROI on predicting participant performance (F1) and classifier performance (AUC) scores (all ps > 0.05) in the SVM classification model. In the KNN model, ROI had a significant influence on the classifier performance [F(1,8.939) = 7.585, p = 0.023]. There was no evidence for artifact correction and ROI selection changing the prediction of participants performance and classifier performance in EEG-based mental MI if using SVM-based classification (71-100% correct classifications across different signal preprocessing methods). The variance in the prediction of participant performance was significantly higher when the experiment started with a resting-state compared to a mental MI task block [X2(1) = 5.849, p = 0.016]. Discussion Overall, we could show that classification is stable across different modes of EEG signal preprocessing when using SVM models. Exploratory analysis gave a hint toward potential effects of the sequence of task execution on the prediction of participant performance, which should be taken into account in future studies.
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Affiliation(s)
- Martin Justinus Rosenfelder
- Institute of Psychology and Education, Clinical and Biological Psychology, Ulm University, Ulm, Germany
- Therapiezentrum Burgau, Burgau, Germany
| | - Myra Spiliopoulou
- Knowledge Management and Discovery Lab, Faculty of Computer Science, Otto-von-Guericke-University Magdeburg, Magdeburg, Germany
| | | | - Rüdiger Pryss
- Institute of Databases and Information Systems, Ulm University, Ulm, Germany
- Institute of Clinical Epidemiology and Biometry, University of Würzburg, Würzburg, Germany
| | - Patrick Fissler
- Institute of Psychology and Education, Clinical and Biological Psychology, Ulm University, Ulm, Germany
- Psychiatric Services Thurgau, Münsterlingen, Switzerland
- University Hospital for Psychiatry and Psychotherapy, Paracelsus Medical University, Salzburg, Austria
| | - Mario della Piedra Walter
- Therapiezentrum Burgau, Burgau, Germany
- Faculty 2: Biology/Chemistry, University of Bremen, Bremen, Germany
| | - Iris-Tatjana Kolassa
- Institute of Psychology and Education, Clinical and Biological Psychology, Ulm University, Ulm, Germany
| | - Andreas Bender
- Therapiezentrum Burgau, Burgau, Germany
- Department of Neurology, University of Munich, Munich, Germany
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Velasco I, Sipols A, De Blas CS, Pastor L, Bayona S. Motor imagery EEG signal classification with a multivariate time series approach. Biomed Eng Online 2023; 22:29. [PMID: 36959601 PMCID: PMC10035287 DOI: 10.1186/s12938-023-01079-x] [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: 05/25/2022] [Accepted: 02/10/2023] [Indexed: 03/25/2023] Open
Abstract
BACKGROUND Electroencephalogram (EEG) signals record electrical activity on the scalp. Measured signals, especially EEG motor imagery signals, are often inconsistent or distorted, which compromises their classification accuracy. Achieving a reliable classification of motor imagery EEG signals opens the door to possibilities such as the assessment of consciousness, brain computer interfaces or diagnostic tools. We seek a method that works with a reduced number of variables, in order to avoid overfitting and to improve interpretability. This work aims to enhance EEG signal classification accuracy by using methods based on time series analysis. Previous work on this line, usually took a univariate approach, thus losing the possibility to take advantage of the correlation information existing within the time series provided by the different electrodes. To overcome this problem, we propose a multivariate approach that can fully capture the relationships among the different time series included in the EEG data. To perform the multivariate time series analysis, we use a multi-resolution analysis approach based on the discrete wavelet transform, together with a stepwise discriminant that selects the most discriminant variables provided by the discrete wavelet transform analysis RESULTS: Applying this methodology to EEG data to differentiate between the motor imagery tasks of moving either hands or feet has yielded very good classification results, achieving in some cases up to 100% of accuracy for this 2-class pre-processed dataset. Besides, the fact that these results were achieved using a reduced number of variables (55 out of 22,176) can shed light on the relevance and impact of those variables. CONCLUSIONS This work has a potentially large impact, as it enables classification of EEG data based on multivariate time series analysis in an interpretable way with high accuracy. The method allows a model with a reduced number of features, facilitating its interpretability and improving overfitting. Future work will extend the application of this classification method to help in diagnosis procedures for detecting brain pathologies and for its use in brain computer interfaces. In addition, the results presented here suggest that this method could be applied to other fields for the successful analysis of multivariate temporal data.
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Affiliation(s)
- I Velasco
- Department of Computer Science and Statistics, Rey Juan Carlos University, Madrid, Spain.
| | - A Sipols
- Department of Applied Mathematics, Science and Engineering of Materials and Electronic Technology, Rey Juan Carlos University, Madrid, Spain
| | - C Simon De Blas
- Department of Computer Science and Statistics, Rey Juan Carlos University, Madrid, Spain
| | - L Pastor
- Department of Computer Science and Statistics, Rey Juan Carlos University, Madrid, Spain
- Center for Computational Simulation, Universidad Politecnica de Madrid, Madrid, Spain
| | - S Bayona
- Department of Computer Science and Statistics, Rey Juan Carlos University, Madrid, Spain
- Center for Computational Simulation, Universidad Politecnica de Madrid, Madrid, Spain
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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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Pan J, Xie Q, Qin P, Chen Y, He Y, Huang H, Wang F, Ni X, Cichocki A, Yu R, Li Y. Prognosis for patients with cognitive motor dissociation identified by brain-computer interface. Brain 2020; 143:1177-1189. [PMID: 32101603 PMCID: PMC7174053 DOI: 10.1093/brain/awaa026] [Citation(s) in RCA: 71] [Impact Index Per Article: 17.8] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/04/2019] [Revised: 12/08/2019] [Accepted: 12/17/2019] [Indexed: 01/15/2023] Open
Abstract
Cognitive motor dissociation describes a subset of patients with disorders of consciousness who show neuroimaging evidence of consciousness but no detectable command-following behaviours. Although essential for family counselling, decision-making, and the design of rehabilitation programmes, the prognosis for patients with cognitive motor dissociation remains under-investigated. The current study included 78 patients with disorders of consciousness who showed no detectable command-following behaviours. These patients included 45 patients with unresponsive wakefulness syndrome and 33 patients in a minimally conscious state, as diagnosed using the Coma Recovery Scale-Revised. Each patient underwent an EEG-based brain-computer interface experiment, in which he or she was instructed to perform an item-selection task (i.e. select a photograph or a number from two candidates). Patients who achieved statistically significant brain-computer interface accuracies were identified as cognitive motor dissociation. Two evaluations using the Coma Recovery Scale-Revised, one before the experiment and the other 3 months later, were carried out to measure the patients' behavioural improvements. Among the 78 patients with disorders of consciousness, our results showed that within the unresponsive wakefulness syndrome patient group, 15 of 18 patients with cognitive motor dissociation (83.33%) regained consciousness, while only five of the other 27 unresponsive wakefulness syndrome patients without significant brain-computer interface accuracies (18.52%) regained consciousness. Furthermore, within the minimally conscious state patient group, 14 of 16 patients with cognitive motor dissociation (87.5%) showed improvements in their Coma Recovery Scale-Revised scores, whereas only four of the other 17 minimally conscious state patients without significant brain-computer interface accuracies (23.53%) had improved Coma Recovery Scale-Revised scores. Our results suggest that patients with cognitive motor dissociation have a better outcome than other patients. Our findings extend current knowledge of the prognosis for patients with cognitive motor dissociation and have important implications for brain-computer interface-based clinical diagnosis and prognosis for patients with disorders of consciousness.
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Affiliation(s)
- Jiahui Pan
- Center for Brain-Computer Interfaces and Brain Information Processing, South China University of Technology, Guangzhou, China
- School of Software, South China Normal University, Guangzhou, China
| | - Qiuyou Xie
- Department of Rehabilitation Medicine, Zhujiang Hospital, Southern Medical University, Guangzhou, China
- Centre for Hyperbaric Oxygen and Neurorehabilitation, Guangzhou General Hospital of Guangzhou Military Command, Guangzhou, China
| | - Pengmin Qin
- Centre for Studies of Psychological Applications, Guangdong Key Laboratory of Mental Health and Cognitive Science, School of Psychology, South China Normal University, Guangzhou, China
| | - Yan Chen
- Centre for Hyperbaric Oxygen and Neurorehabilitation, Guangzhou General Hospital of Guangzhou Military Command, Guangzhou, China
| | - Yanbin He
- Centre for Hyperbaric Oxygen and Neurorehabilitation, Guangzhou General Hospital of Guangzhou Military Command, Guangzhou, China
- Department of Traumatic Brain Injury Rehabilitation and Severe Rehabilitation, Guangdong Work Injury Rehabilitation Hospital, Guangzhou, China
| | - Haiyun Huang
- Center for Brain-Computer Interfaces and Brain Information Processing, South China University of Technology, Guangzhou, China
| | - Fei Wang
- Center for Brain-Computer Interfaces and Brain Information Processing, South China University of Technology, Guangzhou, China
- School of Software, South China Normal University, Guangzhou, China
| | - Xiaoxiao Ni
- Centre for Hyperbaric Oxygen and Neurorehabilitation, Guangzhou General Hospital of Guangzhou Military Command, Guangzhou, China
| | - Andrzej Cichocki
- Skolkovo Institute of Science and Technology (Skoltech), Moscow 143026, Russia
- Nicolaus Copernicus University (UMK), Torun 87-100, Poland
| | - Ronghao Yu
- Centre for Hyperbaric Oxygen and Neurorehabilitation, Guangzhou General Hospital of Guangzhou Military Command, Guangzhou, China
| | - Yuanqing Li
- Center for Brain-Computer Interfaces and Brain Information Processing, South China University of Technology, Guangzhou, China
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Gabriel D, Henriques J, Comte A, Grigoryeva L, Ortega JP, Cretin E, Brunotte G, Haffen E, Moulin T, Aubry R, Pazart L. Substitute or complement? Defining the relative place of EEG and fMRI in the detection of voluntary brain reactions. Neuroscience 2015; 290:435-44. [DOI: 10.1016/j.neuroscience.2015.01.053] [Citation(s) in RCA: 6] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/04/2014] [Revised: 01/17/2015] [Accepted: 01/22/2015] [Indexed: 10/24/2022]
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Pazart L, Gabriel D, Cretin E, Aubry R. Neuroimaging for detecting covert awareness in patients with disorders of consciousness: reinforce the place of clinical feeling! Front Hum Neurosci 2015; 9:78. [PMID: 25741272 PMCID: PMC4330787 DOI: 10.3389/fnhum.2015.00078] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Download PDF] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/20/2014] [Accepted: 01/31/2015] [Indexed: 11/13/2022] Open
Affiliation(s)
- Lionel Pazart
- Clinical Investigation Centre, Inserm/University Hospital, Inserm CIC 1431Besançon, France
- *Correspondence:
| | - Damien Gabriel
- Clinical Investigation Centre, Inserm/University Hospital, Inserm CIC 1431Besançon, France
- Laboratoire de Neurosciences de Besançon EA-481, University of Franche-Comté, SFR FED 4231Besançon, France
| | - Elodie Cretin
- Clinical Investigation Centre, Inserm/University Hospital, Inserm CIC 1431Besançon, France
- Espace Ethique Bourgogne and Franche-ComtéBesançon, France
| | - Regis Aubry
- Clinical Investigation Centre, Inserm/University Hospital, Inserm CIC 1431Besançon, France
- Espace Ethique Bourgogne and Franche-ComtéBesançon, France
- Pain and Palliative Medicine Department, University Hospital of BesançonBesançon, France
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