Reaction Time Predicts Brain–Computer Interface Aptitude

Darvishi, Sam; Gharabaghi, Alireza; Ridding, Michael C.; Abbott, Derek; Baumert, Mathias

doi:10.1109/jtehm.2018.2875985

Cited by 10 publications

(8 citation statements)

References 44 publications

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“…The ROC analysis showed that patients who could press and release the LeMMS at least 16 times in 30 s were able to use the scanning system. This result is in line with Darvishi et al who demonstrated that simple reaction time is correlated with BCI performance [28]. In other words, the simple reaction time and the Click-Test-30 (a measure analogous to simple reaction times, in our opinion) are compatible tests for predicting people able to operate their communication technology.…”

Section: Discussionsupporting

confidence: 89%

Development of a new high sensitivity mechanical switch for augmentative and alternative communication access in people with amyotrophic lateral sclerosis

Caligari

Godi

Giardini

et al. 2019

J NeuroEngineering Rehabil

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BackgroundPeople with Amyotrophic Lateral Sclerosis (PwALS) in the advanced phase are critically affected by an almost total loss of mobility and severe communication problems. Scanning access based on the patient’s interaction with a sensor (or switch) that intercepts even a weak body movement is a valid communication aid. However, its use becomes limited with the progressive decline of residual movements. To overcome this problem, we designed a new sensor, the Lever Magnetic-spring Mechanical Switch (LeMMS), allowing repeated activation/release cycles requiring a very small activation force.MethodsThe LeMMS was applied and validated in a group of 20 PwALS in an advanced stage of disease. All subjects were regular users of communication aids employing other sensors, but which they could no longer operate their sensors (different from LeMMS). Patients were assessed at baseline (t0) and after one (t1), 6 (t2) and 12 (t3) months. Assessment at t0 included administration of standardized clinical scales, the Click-Test-30 counting the maximum number of LeMMS activations in 30 s, and thumb/fingers strength assessment with the Kendall scale. The QUEST 2.0-Dev questionnaire was administered at t1. Some use-related information and the Click-Test-30 were collected at t1, t2 and t3.ResultsAfter one training session, all patients could operate the LeMMS with minimal residual movement of one finger. At t1, they used it on average 5.45 h/day. The mean score of the QUEST 2.0-Dev was 4.63, suggesting strong satisfaction with the LeMMS. Regarding Click-Test-30 scores, no significant difference was found between t0 and t1, but performance at t2 and t3 declined significantly (p < 0.005 vs. t0). At t3, 9/20 patients were still able to use their communication aid.ConclusionsThis new switch sensor can enable PwALS to use their communication aids for a prolonged time even in the advanced phase of disease. It is easy to use, reliable and cheap, thus representing an intermediate alternative to more sophisticated and costly devices.

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Section: Discussionsupporting

confidence: 89%

Development of a new high sensitivity mechanical switch for augmentative and alternative communication access in people with amyotrophic lateral sclerosis

Caligari

Godi

Giardini

et al. 2019

J NeuroEngineering Rehabil

View full text Add to dashboard Cite

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“…Cognitive and neurological factors including functions and anatomy along with emotional and mental processes give rise to intra-and inter-subject variability affecting the performance of SMR-based BCI (Wens et al, 2014;Reichert et al, 2015;Zhang et al, 2015;Acqualagna et al, 2016;Betzel and Bassett, 2017;Vasilyev et al, 2017;Seghier and Price, 2018;Betzel et al, 2019;Smith et al, 2019). Time-variant cognitive factors such as fatigue, memory load, attention and reaction time modulate instantaneous brain activity, and can cause inconsistent SMRbased BCI performance (Hammer et al, 2012;Ahn and Jun, 2015;Fox et al, 2015;Jeunet et al, 2015;Darvishi et al, 2018;Sannelli et al, 2019). Furthermore, users' characteristics such as lifestyle, gender, and age can influence BCI performance (Ahn and Jun, 2015).…”

Section: Bci Performance Predictorsmentioning

confidence: 99%

Intra- and Inter-subject Variability in EEG-Based Sensorimotor Brain Computer Interface: A Review

Saha

Baumert

2020

Front. Comput. Neurosci.

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178

109

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Brain computer interfaces (BCI) for the rehabilitation of motor impairments exploit sensorimotor rhythms (SMR) in the electroencephalogram (EEG). However, the neurophysiological processes underpinning the SMR often vary over time and across subjects. Inherent intra-and inter-subject variability causes covariate shift in data distributions that impede the transferability of model parameters amongst sessions/subjects. Transfer learning includes machine learning-based methods to compensate for inter-subject and inter-session (intra-subject) variability manifested in EEG-derived feature distributions as a covariate shift for BCI. Besides transfer learning approaches, recent studies have explored psychological and neurophysiological predictors as well as inter-subject associativity assessment, which may augment transfer learning in EEG-based BCI. Here, we highlight the importance of measuring inter-session/subject performance predictors for generalized BCI frameworks for both normal and motor-impaired people, reducing the necessity for tedious and annoying calibration sessions and BCI training.

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“…Therefore, the relationship between spatial abilities and MI-BCI performance should be further validated in future experiments. It would be interesting to look also at reaction times of the test, as Darvishi et al (2018) showed that scores on the reaction time test are related to BCI performance.…”

Section: Spatial Abilitiesmentioning

confidence: 99%

Vividness of Visual Imagery and Personality Impact Motor-Imagery Brain Computer Interfaces

Leeuwis

Paas

Alimardani

2021

Front. Hum. Neurosci.

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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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Reaction Time Predicts Brain–Computer Interface Aptitude

Cited by 10 publications

References 44 publications

Development of a new high sensitivity mechanical switch for augmentative and alternative communication access in people with amyotrophic lateral sclerosis

Development of a new high sensitivity mechanical switch for augmentative and alternative communication access in people with amyotrophic lateral sclerosis

Intra- and Inter-subject Variability in EEG-Based Sensorimotor Brain Computer Interface: A Review

Vividness of Visual Imagery and Personality Impact Motor-Imagery Brain Computer Interfaces

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