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Bosch V, Mecacci G. Eyes on the road: brain computer interfaces and cognitive distraction in traffic. FRONTIERS IN NEUROERGONOMICS 2023; 4:1171910. [PMID: 38234470 PMCID: PMC10790900 DOI: 10.3389/fnrgo.2023.1171910] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 02/22/2023] [Accepted: 05/11/2023] [Indexed: 01/19/2024]
Abstract
Novel wearable neurotechnology is able to provide insight into its wearer's cognitive processes and offers ways to change or enhance their capacities. Moreover, it offers the promise of hands-free device control. These brain-computer interfaces are likely to become an everyday technology in the near future, due to their increasing accessibility and affordability. We, therefore, must anticipate their impact, not only on society and individuals broadly but also more specifically on sectors such as traffic and transport. In an economy where attention is increasingly becoming a scarce good, these innovations may present both opportunities and challenges for daily activities that require focus, such as driving and cycling. Here, we argue that their development carries a dual risk. Firstly, BCI-based devices may match or further increase the intensity of cognitive human-technology interaction over the current hands-free communication devices which, despite being widely accepted, are well-known for introducing a significant amount of cognitive load and distraction. Secondly, BCI-based devices will be typically harder than hands-free devices to both visually detect (e.g., how can law enforcement check when these extremely small and well-integrated devices are used?) and restrain in their use (e.g., how do we prevent users from using such neurotechnologies without breaching personal integrity and privacy?). Their use in traffic should be anticipated by researchers, engineers, and policymakers, in order to ensure the safety of all road users.
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Affiliation(s)
- Victoria Bosch
- Machine Learning, Institute of Cognitive Science, Osnabrück University, Osnabrück, Germany
| | - Giulio Mecacci
- Department of Artificial Intelligence, Donders Institute for Brain, Cognition and Behavior, Radboud University, Nijmegen, Netherlands
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du Bois N, Bigirimana AD, Korik A, Kéthina LG, Rutembesa E, Mutabaruka J, Mutesa L, Prasad G, Jansen S, Coyle DH. Neurofeedback with low-cost, wearable electroencephalography (EEG) reduces symptoms in chronic Post-Traumatic Stress Disorder. J Affect Disord 2021; 295:1319-1334. [PMID: 34706446 DOI: 10.1016/j.jad.2021.08.071] [Citation(s) in RCA: 9] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 01/23/2021] [Revised: 07/19/2021] [Accepted: 08/25/2021] [Indexed: 11/30/2022]
Abstract
BACKGROUND The study examines the effectiveness of both neurofeedback and motor-imagery brain-computer interface (BCI) training, which promotes self-regulation of brain activity, using low-cost electroencephalography (EEG)-based wearable neurotechnology outside a clinical setting, as a potential treatment for post-traumatic stress disorder (PTSD) in Rwanda. METHODS Participants received training/treatment sessions along with a pre- and post- intervention clinical assessment, (N = 29; control n = 9, neurofeedback (NF, 7 sessions) n = 10, and motor-imagery (MI, 6 sessions) n = 10). Feedback was presented visually via a videogame. Participants were asked to regulate (NF) or intentionally modulate (MI) brain activity to affect/control the game. RESULTS The NF group demonstrated an increase in resting-state alpha 8-12 Hz bandpower following individual training sessions, termed alpha 'rebound' (Pz channel, p = 0.025, all channels, p = 0.024), consistent with previous research findings. This alpha 'rebound', unobserved in the MI group, produced a clinically relevant reduction in symptom severity in NF group, as revealed in three of seven clinical outcome measures: PCL-5 (p = 0.005), PTSD screen (p = 0.005), and HTQ (p = 0.005). LIMITATIONS Data collection took place in environments that posed difficulties in controlling environmental factors. Nevertheless, this limitation improves ecological validity, as neurotechnology treatments must be deployable outside controlled environments, to be a feasible technological treatment. CONCLUSIONS The study produced the first evidence to support a low-cost, neurotechnological solution for neurofeedback as an effective treatment of PTSD for victims of acute trauma in conflict zones in a developing country.
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Affiliation(s)
- N du Bois
- Intelligent Systems Research Centre, Ulster University (UU), Magee Campus, NI, United Kingdom
| | - A D Bigirimana
- Intelligent Systems Research Centre, Ulster University (UU), Magee Campus, NI, United Kingdom
| | - A Korik
- Intelligent Systems Research Centre, Ulster University (UU), Magee Campus, NI, United Kingdom
| | - L Gaju Kéthina
- Department of Clinical Psychology, College of Medicine and Health Sciences, University of Rwanda (UR), Huye, Rwanda
| | - E Rutembesa
- Department of Clinical Psychology, College of Medicine and Health Sciences, University of Rwanda (UR), Huye, Rwanda
| | - J Mutabaruka
- Department of Clinical Psychology, College of Medicine and Health Sciences, University of Rwanda (UR), Huye, Rwanda
| | - L Mutesa
- Centre for Human Genetics, School of Medicine and Pharmacy, College of Medicine and Health Sciences, University of Rwanda (UR), Huye, Rwanda
| | - G Prasad
- Intelligent Systems Research Centre, Ulster University (UU), Magee Campus, NI, United Kingdom
| | - S Jansen
- Department of Clinical Psychology, College of Medicine and Health Sciences, University of Rwanda (UR), Huye, Rwanda
| | - D H Coyle
- Intelligent Systems Research Centre, Ulster University (UU), Magee Campus, NI, United Kingdom.
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Guthrie MD, Herrera AJ, Downey JE, Brane LJ, Boninger ML, Collinger JL. The impact of distractions on intracortical brain–computer interface control of a robotic arm. BRAIN-COMPUTER INTERFACES 2021. [DOI: 10.1080/2326263x.2021.1980292] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/19/2022]
Affiliation(s)
- Michael D. Guthrie
- Rehab Neural Engineering Labs, Department of Bioengineering, Pittsburgh, PA, USA
- Department of Physical Medicine and Rehabilitation, University of Pittsburgh Medical Center, Pittsburgh, PA, USA
| | - Angelica J Herrera
- Rehab Neural Engineering Labs, Department of Bioengineering, Pittsburgh, PA, USA
- Department of Bioengineering, University of Pittsburgh, Pittsburgh, PA, USA
- Center for the Neural Basis of Cognition, Department of Neuroscience, University of Pittsburgh, Pittsburgh, PA, USA
| | - John E. Downey
- Rehab Neural Engineering Labs, Department of Bioengineering, Pittsburgh, PA, USA
- Department of Bioengineering, University of Pittsburgh, Pittsburgh, PA, USA
- Department of Organismal Biology and Anatomy, University of Chicago, Chicago, IL, USA
| | - Lucas J. Brane
- Rehab Neural Engineering Labs, Department of Bioengineering, Pittsburgh, PA, USA
- Department of Physical Medicine and Rehabilitation, University of Pittsburgh Medical Center, Pittsburgh, PA, USA
| | - Michael L. Boninger
- Rehab Neural Engineering Labs, Department of Bioengineering, Pittsburgh, PA, USA
- Department of Physical Medicine and Rehabilitation, University of Pittsburgh Medical Center, Pittsburgh, PA, USA
- Department of Bioengineering, University of Pittsburgh, Pittsburgh, PA, USA
- Department of Veterans Affairs, Human Engineering Research Laboratories, Va Center of Excellence, Pittsburgh, Pa, USA
| | - Jennifer L. Collinger
- Rehab Neural Engineering Labs, Department of Bioengineering, Pittsburgh, PA, USA
- Department of Physical Medicine and Rehabilitation, University of Pittsburgh Medical Center, Pittsburgh, PA, USA
- Department of Bioengineering, University of Pittsburgh, Pittsburgh, PA, USA
- Center for the Neural Basis of Cognition, Department of Neuroscience, University of Pittsburgh, Pittsburgh, PA, USA
- Department of Veterans Affairs, Human Engineering Research Laboratories, Va Center of Excellence, Pittsburgh, Pa, USA
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BCI-Based Control for Ankle Exoskeleton T-FLEX: Comparison of Visual and Haptic Stimuli with Stroke Survivors. SENSORS 2021; 21:s21196431. [PMID: 34640750 PMCID: PMC8512904 DOI: 10.3390/s21196431] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 06/01/2021] [Revised: 08/31/2021] [Accepted: 09/15/2021] [Indexed: 11/16/2022]
Abstract
Brain–computer interface (BCI) remains an emerging tool that seeks to improve the patient interaction with the therapeutic mechanisms and to generate neuroplasticity progressively through neuromotor abilities. Motor imagery (MI) analysis is the most used paradigm based on the motor cortex’s electrical activity to detect movement intention. It has been shown that motor imagery mental practice with movement-associated stimuli may offer an effective strategy to facilitate motor recovery in brain injury patients. In this sense, this study aims to present the BCI associated with visual and haptic stimuli to facilitate MI generation and control the T-FLEX ankle exoskeleton. To achieve this, five post-stroke patients (55–63 years) were subjected to three different strategies using T-FLEX: stationary therapy (ST) without motor imagination, motor imagination with visual stimulation (MIV), and motor imagination with visual-haptic inducement (MIVH). The quantitative characterization of both BCI stimuli strategies was made through the motor imagery accuracy rate, the electroencephalographic (EEG) analysis during the MI active periods, the statistical analysis, and a subjective patient’s perception. The preliminary results demonstrated the viability of the BCI-controlled ankle exoskeleton system with the beta rebound, in terms of patient’s performance during MI active periods and satisfaction outcomes. Accuracy differences employing haptic stimulus were detected with an average of 68% compared with the 50.7% over only visual stimulus. However, the power spectral density (PSD) did not present changes in prominent activation of the MI band but presented significant variations in terms of laterality. In this way, visual and haptic stimuli improved the subject’s MI accuracy but did not generate differential brain activity over the affected hemisphere. Hence, long-term sessions with a more extensive sample and a more robust algorithm should be carried out to evaluate the impact of the proposed system on neuronal and motor evolution after stroke.
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Mladenovic J, Frey J, Pramij S, Mattout J, Lotte F. Towards identifying optimal biased feedback for various user states and traits in motor imagery BCI. IEEE Trans Biomed Eng 2021; 69:1101-1110. [PMID: 34543189 DOI: 10.1109/tbme.2021.3113854] [Citation(s) in RCA: 6] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/09/2022]
Abstract
OBJECTIVE Neural self-regulation is necessary for achieving control over brain-computer interfaces (BCIs). This can be an arduous learning process esspecially for motor imagery BCI. Various training methods were proposed to assist users in accomplishing BCI control and increase performance. Notably the use of biased feedback, i.e. non-realistic representation of performance. Benefits of biased feedback on performance and learning vary between users (e.g. depending on their initial level of BCI control) and remain speculative. To disentangle the speculations, we investigate what personality type, initial state and calibration performance (CP) could benefit from biased feedback. METHODS We conduct an experiment (n=30 for 2 sessions). The feedback provided to each group (n=10) is either positively, negatively or not biased. RESULTS Statistical analyses suggest that interactions between bias and: 1) workload, 2) anxiety, and 3) self-control significantly affect online performance. For instance, low initial workload paired with negative bias is associated to higher peak performances (86%) than without any bias (69%). High anxiety relates negatively to performance no matter the bias (60%), while low anxiety matches best with negative bias (76%). For low CP, learning rate (LR) increases with negative bias only short term (LR=2%) as during the second session it severely drops (LR=-1%). CONCLUSION We unveil many interactions between said human factors and bias. Additionally, we use prediction models to confirm and reveal even more interactions. SIGNIFICANCE This paper is a first step towards identifying optimal biased feedback for a personality type, state, and CP in order to maximize BCI performance and learning.
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Hossaini A, Valeriani D, Nam CS, Ferrante R, Mahmud M. A Functional BCI Model by the P2731 working group: Physiology. BRAIN-COMPUTER INTERFACES 2021. [DOI: 10.1080/2326263x.2021.1968665] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/20/2022]
Affiliation(s)
- Ali Hossaini
- Department of Engineering, King’s College London, London, UK
| | | | - Chang S. Nam
- Department of Industrial and Systems Engineering, North Carolina State University, Raleigh, NC, USA
| | | | - Mufti Mahmud
- School of Science and Technology, Nottingham Trent University, Nottingham, UK
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Human-Machine Integration in Processes within Industry 4.0 Management. SENSORS 2021; 21:s21175928. [PMID: 34502819 PMCID: PMC8434634 DOI: 10.3390/s21175928] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 07/11/2021] [Revised: 08/28/2021] [Accepted: 08/30/2021] [Indexed: 12/20/2022]
Abstract
The aim of this work is to use IIoT technology and advanced data processing to promote integration strategies between these elements to achieve a better understanding of the processing of information and thus increase the integrability of the human-machine binomial, enabling appropriate management strategies. Therefore, the major objective of this paper is to evaluate how human-machine integration helps to explain the variability associated with value creation processes. It will be carried out through an action research methodology in two different case studies covering different sectors and having different complexity levels. By covering cases from different sectors and involving different value stream architectures, with different levels of human influence and organisational requirements, it will be possible to assess the transparency increases reached as well as the benefits of analysing processes with higher level of integration between them.
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Garcia A. DE, Sierra M. SD, Gomez-Vargas D, Jiménez MF, Múnera M, Cifuentes CA. Semi-Remote Gait Assistance Interface: A Joystick with Visual Feedback Capabilities for Therapists. SENSORS 2021; 21:s21103521. [PMID: 34069340 PMCID: PMC8158774 DOI: 10.3390/s21103521] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 03/24/2021] [Revised: 05/03/2021] [Accepted: 05/06/2021] [Indexed: 11/21/2022]
Abstract
The constant growth of pathologies affecting human mobility has led to developing of different assistive devices to provide physical and cognitive assistance. Smart walkers are a particular type of these devices since they integrate navigation systems, path-following algorithms, and user interaction modules to ensure natural and intuitive interaction. Although these functionalities are often implemented in rehabilitation scenarios, there is a need to actively involve the healthcare professionals in the interaction loop while guaranteeing safety for them and patients. This work presents the validation of two visual feedback strategies for the teleoperation of a simulated robotic walker during an assisted navigation task. For this purpose, a group of 14 clinicians from the rehabilitation area formed the validation group. A simple path-following task was proposed, and the feedback strategies were assessed through the kinematic estimation error (KTE) and a usability survey. A KTE of 0.28 m was obtained for the feedback strategy on the joystick. Additionally, significant differences were found through a Mann–Whitney–Wilcoxon test for the perception of behavior and confidence towards the joystick according to the modes of interaction (p-values of 0.04 and 0.01, respectively). The use of visual feedback with this tool contributes to research areas such as remote management of therapies and monitoring rehabilitation of people’s mobility.
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Affiliation(s)
- Daniel E. Garcia A.
- Department of Biomedical Engineering, Colombian School of Engineering Julio Garavito, Bogota 111166, Colombia; (D.E.G.A.); (S.D.S.M.); (D.G.-V.); (M.M.)
| | - Sergio D. Sierra M.
- Department of Biomedical Engineering, Colombian School of Engineering Julio Garavito, Bogota 111166, Colombia; (D.E.G.A.); (S.D.S.M.); (D.G.-V.); (M.M.)
| | - Daniel Gomez-Vargas
- Department of Biomedical Engineering, Colombian School of Engineering Julio Garavito, Bogota 111166, Colombia; (D.E.G.A.); (S.D.S.M.); (D.G.-V.); (M.M.)
| | - Mario F. Jiménez
- School of Engineering, Science and Technology, Universidad del Rosario, Bogotá 111711, Colombia
- Correspondence: (M.F.J.); (C.A.C.); Tel.: +57-(1)-297-0200 (M.F.J.); +57-(031)-668-3600 (C.A.C.)
| | - Marcela Múnera
- Department of Biomedical Engineering, Colombian School of Engineering Julio Garavito, Bogota 111166, Colombia; (D.E.G.A.); (S.D.S.M.); (D.G.-V.); (M.M.)
| | - Carlos A. Cifuentes
- Department of Biomedical Engineering, Colombian School of Engineering Julio Garavito, Bogota 111166, Colombia; (D.E.G.A.); (S.D.S.M.); (D.G.-V.); (M.M.)
- Correspondence: (M.F.J.); (C.A.C.); Tel.: +57-(1)-297-0200 (M.F.J.); +57-(031)-668-3600 (C.A.C.)
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Leeuwis N, Paas A, Alimardani M. Vividness of Visual Imagery and Personality Impact Motor-Imagery Brain Computer Interfaces. Front Hum Neurosci 2021; 15:634748. [PMID: 33889080 PMCID: PMC8055841 DOI: 10.3389/fnhum.2021.634748] [Citation(s) in RCA: 11] [Impact Index Per Article: 3.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/28/2020] [Accepted: 03/08/2021] [Indexed: 12/19/2022] Open
Abstract
Brain-computer interfaces (BCIs) are communication bridges between a human brain and external world, enabling humans to interact with their environment without muscle intervention. Their functionality, therefore, depends on both the BCI system and the cognitive capacities of the user. Motor-imagery BCIs (MI-BCI) rely on the users' mental imagination of body movements. However, not all users have the ability to sufficiently modulate their brain activity for control of a MI-BCI; a problem known as BCI illiteracy or inefficiency. The underlying mechanism of this phenomenon and the cause of such difference among users is yet not fully understood. In this study, we investigated the impact of several cognitive and psychological measures on MI-BCI performance. Fifty-five novice BCI-users participated in a left- versus right-hand motor imagery task. In addition to their BCI classification error rate and demographics, psychological measures including personality factors, affinity for technology, and motivation during the experiment, as well as cognitive measures including visuospatial memory and spatial ability and Vividness of Visual Imagery were collected. Factors that were found to have a significant impact on MI-BCI performance were Vividness of Visual Imagery, and the personality factors of orderliness and autonomy. These findings shed light on individual traits that lead to difficulty in BCI operation and hence can help with early prediction of inefficiency among users to optimize training for them.
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Affiliation(s)
- Nikki Leeuwis
- Department of Cognitive Science and Artificial Intelligence, Tilburg University, Tilburg, Netherlands
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Fernández-Rodríguez Á, Ron-Angevin R, Sanz-Arigita EJ, Parize A, Esquirol J, Perrier A, Laur S, André JM, Lespinet-Najib V, Garcia L. Effect of Distracting Background Speech in an Auditory Brain-Computer Interface. Brain Sci 2021; 11:brainsci11010039. [PMID: 33401410 PMCID: PMC7823829 DOI: 10.3390/brainsci11010039] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/06/2020] [Revised: 11/12/2020] [Accepted: 12/23/2020] [Indexed: 11/16/2022] Open
Abstract
Studies so far have analyzed the effect of distractor stimuli in different types of brain-computer interface (BCI). However, the effect of a background speech has not been studied using an auditory event-related potential (ERP-BCI), a convenient option when the visual path cannot be adopted by users. Thus, the aim of the present work is to examine the impact of a background speech on selection performance and user workload in auditory BCI systems. Eleven participants tested three conditions: (i) auditory BCI control condition, (ii) auditory BCI with a background speech to ignore (non-attentional condition), and (iii) auditory BCI while the user has to pay attention to the background speech (attentional condition). The results demonstrated that, despite no significant differences in performance, shared attention to auditory BCI and background speech required a higher cognitive workload. In addition, the P300 target stimuli in the non-attentional condition were significantly higher than those in the attentional condition for several channels. The non-attentional condition was the only condition that showed significant differences in the amplitude of the P300 between target and non-target stimuli. The present study indicates that background speech, especially when it is attended to, is an important interference that should be avoided while using an auditory BCI.
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Affiliation(s)
| | - Ricardo Ron-Angevin
- UMA-BCI Group, Departamento de Tecnología Electrónica, Universidad de Málaga, 29071 Malaga, Spain;
- Correspondence:
| | - Ernesto J. Sanz-Arigita
- Neuro and Aging and Human Cognition, INCIA-UMR 5287-CNRS, Université de Bordeaux, 33076 Bordeaux, France;
| | - Antoine Parize
- Laboratoire IMS, CNRS UMR 5218, Cognitive Team, Bordeaux INP-ENSC, 33400 Talence, France; (A.P.); (J.E.); (A.P.); (S.L.); (J.M.-A.); (V.L.-N.); (L.G.)
| | - Juliette Esquirol
- Laboratoire IMS, CNRS UMR 5218, Cognitive Team, Bordeaux INP-ENSC, 33400 Talence, France; (A.P.); (J.E.); (A.P.); (S.L.); (J.M.-A.); (V.L.-N.); (L.G.)
| | - Alban Perrier
- Laboratoire IMS, CNRS UMR 5218, Cognitive Team, Bordeaux INP-ENSC, 33400 Talence, France; (A.P.); (J.E.); (A.P.); (S.L.); (J.M.-A.); (V.L.-N.); (L.G.)
| | - Simon Laur
- Laboratoire IMS, CNRS UMR 5218, Cognitive Team, Bordeaux INP-ENSC, 33400 Talence, France; (A.P.); (J.E.); (A.P.); (S.L.); (J.M.-A.); (V.L.-N.); (L.G.)
| | - Jean-Marc André
- Laboratoire IMS, CNRS UMR 5218, Cognitive Team, Bordeaux INP-ENSC, 33400 Talence, France; (A.P.); (J.E.); (A.P.); (S.L.); (J.M.-A.); (V.L.-N.); (L.G.)
| | - Véronique Lespinet-Najib
- Laboratoire IMS, CNRS UMR 5218, Cognitive Team, Bordeaux INP-ENSC, 33400 Talence, France; (A.P.); (J.E.); (A.P.); (S.L.); (J.M.-A.); (V.L.-N.); (L.G.)
| | - Liliana Garcia
- Laboratoire IMS, CNRS UMR 5218, Cognitive Team, Bordeaux INP-ENSC, 33400 Talence, France; (A.P.); (J.E.); (A.P.); (S.L.); (J.M.-A.); (V.L.-N.); (L.G.)
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Roc A, Pillette L, Mladenovic J, Benaroch C, N'Kaoua B, Jeunet C, Lotte F. A review of user training methods in brain computer interfaces based on mental tasks. J Neural Eng 2020; 18. [PMID: 33181488 DOI: 10.1088/1741-2552/abca17] [Citation(s) in RCA: 28] [Impact Index Per Article: 7.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/24/2020] [Accepted: 11/12/2020] [Indexed: 12/12/2022]
Abstract
Mental-Tasks based Brain-Computer Interfaces (MT-BCIs) allow their users to interact with an external device solely by using brain signals produced through mental tasks. While MT-BCIs are promising for many applications, they are still barely used outside laboratories due to their lack of reliability. MT-BCIs require their users to develop the ability to self-regulate specific brain signals. However, the human learning process to control a BCI is still relatively poorly understood and how to optimally train this ability is currently under investigation. Despite their promises and achievements, traditional training programs have been shown to be sub-optimal and could be further improved. In order to optimize user training and improve BCI performance, human factors should be taken into account. An interdisciplinary approach should be adopted to provide learners with appropriate and/or adaptive training. In this article, we provide an overview of existing methods for MT-BCI user training - notably in terms of environment, instructions, feedback and exercises. We present a categorization and taxonomy of these training approaches, provide guidelines on how to choose the best methods and identify open challenges and perspectives to further improve MT-BCI user training.
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Affiliation(s)
| | | | | | - Camille Benaroch
- Inria Centre de recherche Bordeaux Sud-Ouest, Talence, 33405, FRANCE
| | - Bernard N'Kaoua
- Handicap, Activity, Cognition, Health, Inserm / University of Bordeaux, Talence, FRANCE
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Gupta SS, Manthalkar RR. Classification of visual cognitive workload using analytic wavelet transform. Biomed Signal Process Control 2020. [DOI: 10.1016/j.bspc.2020.101961] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/24/2022]
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Daeglau M, Wallhoff F, Debener S, Condro IS, Kranczioch C, Zich C. Challenge Accepted? Individual Performance Gains for Motor Imagery Practice with Humanoid Robotic EEG Neurofeedback. SENSORS 2020; 20:s20061620. [PMID: 32183285 PMCID: PMC7146190 DOI: 10.3390/s20061620] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 02/07/2020] [Revised: 03/03/2020] [Accepted: 03/10/2020] [Indexed: 01/10/2023]
Abstract
Optimizing neurofeedback (NF) and brain–computer interface (BCI) implementations constitutes a challenge across many fields and has so far been addressed by, among others, advancing signal processing methods or predicting the user’s control ability from neurophysiological or psychological measures. In comparison, how context factors influence NF/BCI performance is largely unexplored. We here investigate whether a competitive multi-user condition leads to better NF/BCI performance than a single-user condition. We implemented a foot motor imagery (MI) NF with mobile electroencephalography (EEG). Twenty-five healthy, young participants steered a humanoid robot in a single-user condition and in a competitive multi-user race condition using a second humanoid robot and a pseudo competitor. NF was based on 8–30 Hz relative event-related desynchronization (ERD) over sensorimotor areas. There was no significant difference between the ERD during the competitive multi-user condition and the single-user condition but considerable inter-individual differences regarding which condition yielded a stronger ERD. Notably, the stronger condition could be predicted from the participants’ MI-induced ERD obtained before the NF blocks. Our findings may contribute to enhance the performance of NF/BCI implementations and highlight the necessity of individualizing context factors.
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Affiliation(s)
- Mareike Daeglau
- Neurocognition and Functional Neurorehabilitation Group, Neuropsychology Lab, Department of Psychology, School of Medicine and Health Sciences, Carl von Ossietzky University Oldenburg, 26111 Oldenburg, Germany; (C.K.); (C.Z.)
- Correspondence:
| | - Frank Wallhoff
- Institute for Assistive Technologies, Jade University of Applied Science, 26389 Oldenburg, Germany; (F.W.); (I.S.C.)
| | - Stefan Debener
- Neuropsychology Lab, Department of Psychology, School of Medicine and Health Sciences, Carl von Ossietzky University Oldenburg, 26111 Oldenburg, Germany;
- Cluster of Excellence Hearing4All, Carl von Ossietzky University Oldenburg, 26111 Oldenburg, Germany
- Research Center Neurosensory Science, Carl von Ossietzky University Oldenburg, 26111 Oldenburg, Germany
| | - Ignatius Sapto Condro
- Institute for Assistive Technologies, Jade University of Applied Science, 26389 Oldenburg, Germany; (F.W.); (I.S.C.)
| | - Cornelia Kranczioch
- Neurocognition and Functional Neurorehabilitation Group, Neuropsychology Lab, Department of Psychology, School of Medicine and Health Sciences, Carl von Ossietzky University Oldenburg, 26111 Oldenburg, Germany; (C.K.); (C.Z.)
- Research Center Neurosensory Science, Carl von Ossietzky University Oldenburg, 26111 Oldenburg, Germany
| | - Catharina Zich
- Neurocognition and Functional Neurorehabilitation Group, Neuropsychology Lab, Department of Psychology, School of Medicine and Health Sciences, Carl von Ossietzky University Oldenburg, 26111 Oldenburg, Germany; (C.K.); (C.Z.)
- Department of Clinical and Movement Neuroscience, UCL Queen Square Institute of Neurology, London WC1N 3BG, UK;
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Research on Optimization Method of VR Task Scenario Resources Driven by User Cognitive Needs. INFORMATION 2020. [DOI: 10.3390/info11020064] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/16/2022] Open
Abstract
Research was performed in order to improve the efficiency of a user’s access to information and the interactive experience of task selection in a virtual reality (VR) system, reduce the level of a user’s cognitive load, and improve the efficiency of designers in building a VR system. On the basis of user behavior cognition-system resource mapping, a task scenario resource optimization method for VR system based on quality function deployment-convolution neural network (QFD-CNN) was proposed. Firstly, under the guidance of user behavior cognition, the characteristics of multi-channel information resources in a VR system were analyzed, and the correlation matrix of the VR system scenario resource characteristics was constructed based on the design criteria of human–computer interaction, cognition, and low-load demand. Secondly, analytic hierarchy process (AHP)-QFD combined with evaluation matrix is used to output the priority ranking of VR system resource characteristics. Then, the VR system task scenario cognitive load experiment is carried out on users, and the CNN input set and output set data are collected through the experiment, in order to build a CNN system and predict the user cognitive load and satisfaction in the human–computer interaction in the VR system. Finally, combined with the task information interface of a VR system in a smart city, the application research of the system resource feature optimization method under multi-channel cognition is carried out. The results show that the test coefficient CR value of the AHP-QFD model based on cognitive load is less than 0.1, and the MSE of CNN prediction model network is 0.004247, which proves the effectiveness of this model. According to the requirements of the same design task in a VR system, by comparing the scheme formed by the traditional design process with the scheme optimized by the method in this paper, the results show that the user has a lower cognitive load and better task operation experience when interacting with the latter scheme, so the optimization method studied in this paper can provide a reference for the system construction of virtual reality.
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