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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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2
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Yoshimura N, Umetsu K, Tonin A, Maruyama Y, Harada K, Rana A, Ganesh G, Chaudhary U, Koike Y, Birbaumer N. Binary Semantic Classification Using Cortical Activation with Pavlovian-Conditioned Vestibular Responses in Healthy and Locked-In Individuals. Cereb Cortex Commun 2021; 2:tgab046. [PMID: 34447933 PMCID: PMC8382900 DOI: 10.1093/texcom/tgab046] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/18/2021] [Revised: 07/01/2021] [Accepted: 07/04/2021] [Indexed: 11/14/2022] Open
Abstract
To develop a more reliable brain–computer interface (BCI) for patients in the completely locked-in state (CLIS), here we propose a Pavlovian conditioning paradigm using galvanic vestibular stimulation (GVS), which can induce a strong sensation of equilibrium distortion in individuals. We hypothesized that associating two different sensations caused by two-directional GVS with the thoughts of “yes” and “no” by individuals would enable us to emphasize the differences in brain activity associated with the thoughts of yes and no and hence help us better distinguish the two from electroencephalography (EEG). We tested this hypothesis with 11 healthy and 1 CLIS participant. Our results showed that, first, conditioning of GVS with the thoughts of yes and no is possible. And second, the classification of whether an individual is thinking “yes” or “no” is significantly improved after the conditioning, even in the absence of subsequent GVS stimulations. We observed average classification accuracy of 73.0% over 11 healthy individuals and 85.3% with the CLIS patient. These results suggest the establishment of GVS-based Pavlovian conditioning and its usability as a noninvasive BCI.
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Affiliation(s)
- Natsue Yoshimura
- Institute of Innovative Research, Tokyo Institute of Technology, Yokohama 226-8503, Japan
| | - Kaito Umetsu
- Institute of Innovative Research, Tokyo Institute of Technology, Yokohama 226-8503, Japan
| | - Alessandro Tonin
- Wyss-Center for Bio and NeuroEngineering, Geneva CH-1202, Switzerland
| | - Yasuhisa Maruyama
- Institute of Innovative Research, Tokyo Institute of Technology, Yokohama 226-8503, Japan
| | - Kyosuke Harada
- Institute of Innovative Research, Tokyo Institute of Technology, Yokohama 226-8503, Japan
| | - Aygul Rana
- Institute of Medical Psychology and Behavioral Neurobiology, University of Tübingen, 72076 Tübingen, Germany
| | - Gowrishankar Ganesh
- Laboratorie d'Informatique, de Robotique et de Microelectronique de Montpellier, U. Montpellier, CNRS, 34095 Montpellier, France
| | - Ujwal Chaudhary
- Institute of Medical Psychology and Behavioral Neurobiology, University of Tübingen, 72076 Tübingen, Germany
| | - Yasuharu Koike
- Institute of Innovative Research, Tokyo Institute of Technology, Yokohama 226-8503, Japan
| | - Niels Birbaumer
- Institute of Medical Psychology and Behavioral Neurobiology, University of Tübingen, 72076 Tübingen, Germany
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Soekadar SR, Kohl SH, Mihara M, von Lühmann A. Optical brain imaging and its application to neurofeedback. Neuroimage Clin 2021; 30:102577. [PMID: 33545580 PMCID: PMC7868728 DOI: 10.1016/j.nicl.2021.102577] [Citation(s) in RCA: 15] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/01/2020] [Revised: 12/30/2020] [Accepted: 01/15/2021] [Indexed: 12/30/2022]
Abstract
Besides passive recording of brain electric or magnetic activity, also non-ionizing electromagnetic or optical radiation can be used for real-time brain imaging. Here, changes in the radiation's absorption or scattering allow for continuous in vivo assessment of regional neurometabolic and neurovascular activity. Besides magnetic resonance imaging (MRI), over the last years, also functional near-infrared spectroscopy (fNIRS) was successfully established in real-time metabolic brain imaging. In contrast to MRI, fNIRS is portable and can be applied at bedside or in everyday life environments, e.g., to restore communication and movement. Here we provide a comprehensive overview of the history and state-of-the-art of real-time optical brain imaging with a special emphasis on its clinical use towards neurofeedback and brain-computer interface (BCI) applications. Besides pointing to the most critical challenges in clinical use, also novel approaches that combine real-time optical neuroimaging with other recording modalities (e.g. electro- or magnetoencephalography) are described, and their use in the context of neuroergonomics, neuroenhancement or neuroadaptive systems discussed.
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Affiliation(s)
- Surjo R Soekadar
- Clinical Neurotechnology Laboratory, Dept. of Psychiatry and Psychotherapy, Neuroscience Research Center, Campus Charité Mitte (CCM), Charité - University Medicine of Berlin, Berlin, Germany.
| | - Simon H Kohl
- JARA-Institute Molecular Neuroscience and Neuroimaging (INM-11), Jülich Research Centre, Jülich, Germany; Child Neuropsychology Section, Department of Child and Adolescent Psychiatry, Psychosomatics and Psychotherapy, Medical Faculty, RWTH Aachen University, Germany
| | - Masahito Mihara
- Department of Neurology, Kawasaki Medical School, Kurashiki-City, Okayama, Japan
| | - Alexander von Lühmann
- Machine Learning Department, Computer Science, Technische Universität Berlin, Berlin, Germany; Neurophotonics Center, Biomedical Engineering, Boston University, Boston, USA
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Covert Intention to Answer "Yes" or "No" Can Be Decoded from Single-Trial Electroencephalograms (EEGs). COMPUTATIONAL INTELLIGENCE AND NEUROSCIENCE 2020; 2019:4259369. [PMID: 31379934 PMCID: PMC6652077 DOI: 10.1155/2019/4259369] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 03/19/2019] [Revised: 05/14/2019] [Accepted: 06/13/2019] [Indexed: 11/18/2022]
Abstract
Interpersonal communication is based on questions and answers, and the most useful and simplest case is the binary “yes or no” question and answer. The purpose of this study is to show that it is possible to decode intentions on “yes” or “no” answers from multichannel single-trial electroencephalograms, which were recorded while covertly answering to self-referential questions with either “yes” or “no.” The intention decoding algorithm consists of a common spatial pattern and support vector machine, which are employed for the feature extraction and pattern classification, respectively, after dividing the overall time-frequency range into subwindows of 200 ms × 2 Hz. The decoding accuracy using the information within each subwindow was investigated to find useful temporal and spectral ranges and found to be the highest for 800–1200 ms in the alpha band or 200–400 ms in the theta band. When the features from multiple subwindows were utilized together, the accuracy was significantly increased up to ∼86%. The most useful features for the “yes/no” discrimination was found to be focused in the right frontal region in the theta band and right centroparietal region in the alpha band, which may reflect the violation of autobiographic facts and higher cognitive load for “no” compared to “yes.” Our task requires the subjects to answer self-referential questions just as in interpersonal conversation without any self-regulation of the brain signals or high cognitive efforts, and the “yes” and “no” answers are decoded directly from the brain activities. This implies that the “mind reading” in a true sense is feasible. Beyond its contribution in fundamental understanding of the neural mechanism of human intention, the decoding of “yes” or “no” from brain activities may eventually lead to a natural brain-computer interface.
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Khalili Ardali M, Rana A, Purmohammad M, Birbaumer N, Chaudhary U. Semantic and BCI-performance in completely paralyzed patients: Possibility of language attrition in completely locked in syndrome. BRAIN AND LANGUAGE 2019; 194:93-97. [PMID: 31151035 DOI: 10.1016/j.bandl.2019.05.004] [Citation(s) in RCA: 9] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 08/14/2018] [Revised: 01/14/2019] [Accepted: 05/23/2019] [Indexed: 06/09/2023]
Abstract
Patients with completely locked-in syndrome (CLIS) are incapable of any voluntary muscle movement and do not have any means of communication. Recently functional near infrared spectroscopy (fNIRS) based brain computer interface (BCI) has been successfully used to enable communication with these patients. The developed fNIRS-BCI system relies on the intactness of language comprehension in these patients in all dimensions of language. Interwoven language and motor cortex in brain, and lack of muscular activity in long run, can cause language attrition due to complete immobility in CLIS patients. In this study we have investigated effects of semantic content of sentences presented to a CLIS patient on the performance of the BCI system during a YES/NO paradigm. Comparison of communication success rate in BCI classification between different semantic categories indicate that semantic content of sentences presented to a CLIS patient can affect the BCI performance. Affected concepts are mostly associated with executive words. These findings can be beneficial towards development of more reliable communication device for patients in CLIS. In addition, these results may assist in elucidating the cognitive changes in completely paralyzed patients with the passage of time since the onset of total immovability.
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Affiliation(s)
- Majid Khalili Ardali
- Institute of Medical Psychology and Behavioral Neurobiology, University of Tubingen, Tubingen, Germany; Department of Cognitive Linguistics, Institute for Cognitive Science Studies (ICSS), Tehran, Iran
| | - Aygul Rana
- Institute of Medical Psychology and Behavioral Neurobiology, University of Tubingen, Tubingen, Germany
| | - Mehdi Purmohammad
- Department of Cognitive Linguistics, Institute for Cognitive Science Studies (ICSS), Tehran, Iran
| | - Niels Birbaumer
- Institute of Medical Psychology and Behavioral Neurobiology, University of Tubingen, Tubingen, Germany; Wyss Center for Bio and Neuroengineering, Geneva, Switzerland
| | - Ujwal Chaudhary
- Institute of Medical Psychology and Behavioral Neurobiology, University of Tubingen, Tubingen, Germany; Wyss Center for Bio and Neuroengineering, Geneva, Switzerland.
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Covert Intention to Answer to Self-Referential Questions Is Represented in Alpha-Band Local and Interregional Neural Synchronies. COMPUTATIONAL INTELLIGENCE AND NEUROSCIENCE 2019; 2019:7084186. [PMID: 30723496 PMCID: PMC6339759 DOI: 10.1155/2019/7084186] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.2] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 08/17/2018] [Accepted: 11/28/2018] [Indexed: 11/17/2022]
Abstract
The most fundamental and simplest intention for interpersonal communication may be the intentions to answer “yes” or “no” to a question, based on a binary decision. However, the neural mechanism of this type of intention has not been investigated in detail. The main purpose of this study was to investigate cortical processing of the “yes/no” intentions to answer self-referential questions. Multichannel electroencephalograms (EEGs) were recorded while covertly answering self-referential questions with either “yes” or “no”. Event-related spectral perturbation (ERSP) and interregional phase synchrony (PS) were investigated to identify the differences in local and global neural synchronies between two intentions. We found that the local and interregional neural synchronies in the alpha-band were significantly different between “yes” and “no,” especially at the period of retaining the intention in mind, which was greater for “no” than for “yes.” These results can be interpreted to signify that a higher cognitive load during working memory retention or higher attentional demand is required for the “no” intention compared to “yes.” Our findings suggest that both local and global neural synchronies in the alpha-band may be significantly differentiated during a critical temporal epoch, according to the contents of the mental representation of the intention.
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Abiri R, Borhani S, Sellers EW, Jiang Y, Zhao X. A comprehensive review of EEG-based brain–computer interface paradigms. J Neural Eng 2019; 16:011001. [DOI: 10.1088/1741-2552/aaf12e] [Citation(s) in RCA: 270] [Impact Index Per Article: 54.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/19/2022]
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Dong SY, Kim BK, Lee SY. EEG-Based Classification of Implicit Intention During Self-Relevant Sentence Reading. IEEE TRANSACTIONS ON CYBERNETICS 2016; 46:2535-2542. [PMID: 26441465 DOI: 10.1109/tcyb.2015.2479240] [Citation(s) in RCA: 9] [Impact Index Per Article: 1.1] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/05/2023]
Abstract
From electroencephalography (EEG) data during self-relevant sentence reading, we were able to discriminate two implicit intentions: 1) "agreement" and 2) "disagreement" to the read sentence. To improve the classification accuracy, discriminant features were selected based on Fisher score among EEG frequency bands and electrodes. Especially, the time-frequency representation with Morlet wavelet transforms showed clear differences in gamma, beta, and alpha band powers at frontocentral area, and theta band power at centroparietal area. The best classification accuracy of 75.5% was obtained by a support vector machine classifier with the gamma band features at frontocentral area. This result may enable a new intelligent user-interface which understands users' implicit intention, i.e., unexpressed or hidden intention.
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van der Heiden L, Liberati G, Sitaram R, Kim S, Jaśkowski P, Raffone A, Olivetti Belardinelli M, Birbaumer N, Veit R. Insula and inferior frontal triangularis activations distinguish between conditioned brain responses using emotional sounds for basic BCI communication. Front Behav Neurosci 2014; 8:247. [PMID: 25100958 PMCID: PMC4104703 DOI: 10.3389/fnbeh.2014.00247] [Citation(s) in RCA: 9] [Impact Index Per Article: 0.9] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/12/2014] [Accepted: 06/30/2014] [Indexed: 11/18/2022] Open
Abstract
In order to enable communication through a brain-computer interface (BCI), it is necessary to discriminate between distinct brain responses. As a first step, we probed the possibility to discriminate between affirmative (“yes”) and negative (“no”) responses using a semantic classical conditioning paradigm, within an fMRI setting. Subjects were presented with congruent and incongruent word-pairs as conditioned stimuli (CS), respectively eliciting affirmative and negative responses. Incongruent word-pairs were associated to an unpleasant unconditioned stimulus (scream, US1) and congruent word-pairs were associated to a pleasant unconditioned stimulus (baby-laughter, US2), in order to elicit emotional conditioned responses (CR). The aim was to discriminate between affirmative and negative responses, enabled by their association with the positive and negative affective stimuli. In the late acquisition phase, when the US were not present anymore, there was a strong significant differential activation for incongruent and congruent word-pairs in a cluster comprising the left insula and the inferior frontal triangularis. This association was not found in the habituation phase. These results suggest that the difference in affirmative and negative brain responses was established as an effect of conditioning, allowing to further investigate the possibility of using this paradigm for a binary choice BCI.
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Affiliation(s)
- Linda van der Heiden
- Department of Cognitive Psychology, University of Finance and Management Pawia, Warsaw, Poland ; Institute of Medical Psychology and Behavioral Neurobiology, Eberhard Karls-University Tübingen, Germany
| | - Giulia Liberati
- Institute of Medical Psychology and Behavioral Neurobiology, Eberhard Karls-University Tübingen, Germany ; Interuniversity Centre for Research on Cognitive Processing in Natural and Artificial Systems (ECONA) Rome, Italy ; Institute of Neuroscience, Université Catholique de Louvain Brussels, Louvain-la-Neuve, Belgium
| | - Ranganatha Sitaram
- Institute of Medical Psychology and Behavioral Neurobiology, Eberhard Karls-University Tübingen, Germany ; Department of Biomedical Engineering, University of Florida Gainesville, FL, USA ; Biomedical Engineering, Sri Chitra Tirunal Institute of Medical Sciences and Technology Trivandrum, India
| | - Sunjung Kim
- Institute of Medical Psychology and Behavioral Neurobiology, Eberhard Karls-University Tübingen, Germany
| | - Piotr Jaśkowski
- Department of Cognitive Psychology, University of Finance and Management Pawia, Warsaw, Poland
| | - Antonino Raffone
- Interuniversity Centre for Research on Cognitive Processing in Natural and Artificial Systems (ECONA) Rome, Italy ; Department of Psychology, University "Sapienza" of Rome Rome, Italy
| | - Marta Olivetti Belardinelli
- Interuniversity Centre for Research on Cognitive Processing in Natural and Artificial Systems (ECONA) Rome, Italy ; Department of Psychology, University "Sapienza" of Rome Rome, Italy
| | - Niels Birbaumer
- Institute of Medical Psychology and Behavioral Neurobiology, Eberhard Karls-University Tübingen, Germany ; Ospedale San Camillo-IRCCS, Istituto di Ricovero e Cura a Carattere Scientifico Venezia Lido, Italy
| | - Ralf Veit
- Institute of Medical Psychology and Behavioral Neurobiology, Eberhard Karls-University Tübingen, Germany
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Halder S, Ruf CA, Furdea A, Pasqualotto E, De Massari D, van der Heiden L, Bogdan M, Rosenstiel W, Birbaumer N, Kübler A, Matuz T. Prediction of P300 BCI aptitude in severe motor impairment. PLoS One 2013; 8:e76148. [PMID: 24204597 PMCID: PMC3799852 DOI: 10.1371/journal.pone.0076148] [Citation(s) in RCA: 14] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/17/2013] [Accepted: 08/20/2013] [Indexed: 12/14/2022] Open
Abstract
Brain-computer interfaces (BCIs) provide a non-muscular communication channel for persons with severe motor impairments. Previous studies have shown that the aptitude with which a BCI can be controlled varies from person to person. A reliable predictor of performance could facilitate selection of a suitable BCI paradigm. Eleven severely motor impaired participants performed three sessions of a P300 BCI web browsing task. Before each session auditory oddball data were collected to predict the BCI aptitude of the participants exhibited in the current session. We found a strong relationship of early positive and negative potentials around 200 ms (elicited with the auditory oddball task) with performance. The amplitude of the P2 (r = −0.77) and of the N2 (r = −0.86) had the strongest correlations. Aptitude prediction using an auditory oddball was successful. The finding that the N2 amplitude is a stronger predictor of performance than P3 amplitude was reproduced after initially showing this effect with a healthy sample of BCI users. This will reduce strain on the end-users by minimizing the time needed to find suitable paradigms and inspire new approaches to improve performance.
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Affiliation(s)
- Sebastian Halder
- Institute of Psychology, University of Würzburg, Würzburg, Germany
- Institute of Medical Psychology and Behavioral Neurobiology, University of Tübingen, Tübingen, Germany
- Department of Computer Engineering, University of Tübingen, Tübingen, Germany
- * E-mail:
| | - Carolin Anne Ruf
- Institute of Medical Psychology and Behavioral Neurobiology, University of Tübingen, Tübingen, Germany
| | - Adrian Furdea
- Institute of Medical Psychology and Behavioral Neurobiology, University of Tübingen, Tübingen, Germany
| | - Emanuele Pasqualotto
- Psychological Sciences Research Institute, Université catholique de Louvain, Louvain-la-neuve, Belgium
| | - Daniele De Massari
- Institute of Medical Psychology and Behavioral Neurobiology, University of Tübingen, Tübingen, Germany
- Ospedale San Camillo, Istituto Di Ricovero e Cura a Carattere Scientifico Fondazione, Venezia-Lido, Italy
- Graduate Training Centre of Neuroscience, International Max Planck Research School, Tübingen, Germany
| | - Linda van der Heiden
- Institute of Medical Psychology and Behavioral Neurobiology, University of Tübingen, Tübingen, Germany
- Department of Cognitive Psychology, University of Finance and Management, Warsaw, Poland
| | - Martin Bogdan
- Department of Computer Engineering, University of Tübingen, Tübingen, Germany
- Computer Engineering, University of Leipzig, Leipzig, Germany
| | - Wolfgang Rosenstiel
- Department of Computer Engineering, University of Tübingen, Tübingen, Germany
| | - Niels Birbaumer
- Institute of Medical Psychology and Behavioral Neurobiology, University of Tübingen, Tübingen, Germany
- Ospedale San Camillo, Istituto Di Ricovero e Cura a Carattere Scientifico Fondazione, Venezia-Lido, Italy
| | - Andrea Kübler
- Institute of Psychology, University of Würzburg, Würzburg, Germany
| | - Tamara Matuz
- Institute of Medical Psychology and Behavioral Neurobiology, University of Tübingen, Tübingen, Germany
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De Massari D, Ruf CA, Furdea A, Matuz T, van der Heiden L, Halder S, Silvoni S, Birbaumer N. Brain communication in the locked-in state. ACTA ACUST UNITED AC 2013; 136:1989-2000. [PMID: 23625062 DOI: 10.1093/brain/awt102] [Citation(s) in RCA: 65] [Impact Index Per Article: 5.9] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/15/2022]
Abstract
Patients in the completely locked-in state have no means of communication and they represent the target population for brain-computer interface research in the last 15 years. Although different paradigms have been tested and different physiological signals used, to date no sufficiently documented completely locked-in state patient was able to control a brain-computer interface over an extended time period. We introduce Pavlovian semantic conditioning to enable basic communication in completely locked-in state. This novel paradigm is based on semantic conditioning for online classification of neuroelectric or any other physiological signals to discriminate between covert (cognitive) 'yes' and 'no' responses. The paradigm comprised the presentation of affirmative and negative statements used as conditioned stimuli, while the unconditioned stimulus consisted of electrical stimulation of the skin paired with affirmative statements. Three patients with advanced amyotrophic lateral sclerosis participated over an extended time period, one of which was in a completely locked-in state, the other two in the locked-in state. The patients' level of vigilance was assessed through auditory oddball procedures to study the correlation between vigilance level and the classifier's performance. The average online classification accuracies of slow cortical components of electroencephalographic signals were around chance level for all the patients. The use of a non-linear classifier in the offline classification procedure resulted in a substantial improvement of the accuracy in one locked-in state patient achieving 70% correct classification. A reliable level of performance in the completely locked-in state patient was not achieved uniformly throughout the 37 sessions despite intact cognitive processing capacity, but in some sessions communication accuracies up to 70% were achieved. Paradigm modifications are proposed. Rapid drop of vigilance was detected suggesting attentional variations or variations of circadian period as important factors in brain-computer interface communication with locked-in state and completely locked-in state.
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Affiliation(s)
- Daniele De Massari
- Institute of Medical Psychology and Behavioral Neurobiology, University of Tübingen, Germany.
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Halder S, Varkuti B, Bogdan M, Kübler A, Rosenstiel W, Sitaram R, Birbaumer N. Prediction of brain-computer interface aptitude from individual brain structure. Front Hum Neurosci 2013; 7:105. [PMID: 23565083 PMCID: PMC3613602 DOI: 10.3389/fnhum.2013.00105] [Citation(s) in RCA: 66] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/02/2013] [Accepted: 03/13/2013] [Indexed: 12/14/2022] Open
Abstract
OBJECTIVE Brain-computer interface (BCI) provide a non-muscular communication channel for patients with impairments of the motor system. A significant number of BCI users is unable to obtain voluntary control of a BCI-system in proper time. This makes methods that can be used to determine the aptitude of a user necessary. METHODS We hypothesized that integrity and connectivity of involved white matter connections may serve as a predictor of individual BCI-performance. Therefore, we analyzed structural data from anatomical scans and DTI of motor imagery BCI-users differentiated into high and low BCI-aptitude groups based on their overall performance. RESULTS Using a machine learning classification method we identified discriminating structural brain trait features and correlated the best features with a continuous measure of individual BCI-performance. Prediction of the aptitude group of each participant was possible with near perfect accuracy (one error). CONCLUSIONS Tissue volumetric analysis yielded only poor classification results. In contrast, the structural integrity and myelination quality of deep white matter structures such as the Corpus Callosum, Cingulum, and Superior Fronto-Occipital Fascicle were positively correlated with individual BCI-performance. SIGNIFICANCE This confirms that structural brain traits contribute to individual performance in BCI use.
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Affiliation(s)
- S. Halder
- Department of Psychology I, University of WürzburgWürzburg, Germany
- Institute of Medical Psychology and Behavioral Neurobiology, University of TübingenTübingen, Germany
- Wilhelm-Schickard Institute for Computer Science, University of TübingenTübingen, Germany
| | - B. Varkuti
- Department of Psychology I, University of WürzburgWürzburg, Germany
| | - M. Bogdan
- Wilhelm-Schickard Institute for Computer Science, University of TübingenTübingen, Germany
- Department of Computer Engineering, University of LeipzigLeipzig, Germany
| | - A. Kübler
- Department of Psychology I, University of WürzburgWürzburg, Germany
| | - W. Rosenstiel
- Wilhelm-Schickard Institute for Computer Science, University of TübingenTübingen, Germany
| | - R. Sitaram
- Department of Biomedical Engineering, University of FloridaGainesville, FL, USA
| | - N. Birbaumer
- Institute of Medical Psychology and Behavioral Neurobiology, University of TübingenTübingen, Germany
- Ospedale San Camillo, Laboratorio di Neuroscience Comportamentale, Istituto di Ricovero e Cura a Carattere ScientificoVenezia, Italy
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