Neural mechanisms of training an auditory event‐related potential task in a brain–computer interface context

Halder, Sebastian; Leinfelder, Teresa; Schulz, Stefan M.; Kübler, Andrea

doi:10.1002/hbm.24531

Cited by 20 publications

(14 citation statements)

References 76 publications

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“…Around 15–30% of individuals are inherently not able to produce brain signals robust enough to operate a BCI (Blankertz et al, 2009 ; Halder et al, 2019 ; Cecotti, 2020 ). Considering neurophysiological phenomena may reduce BCI illiteracy.…”

Section: Challengesmentioning

confidence: 99%

Progress in Brain Computer Interface: Challenges and Opportunities

Saha

Mamun

Ahmed

et al. 2021

Front. Syst. Neurosci.

227

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Brain computer interfaces (BCI) provide a direct communication link between the brain and a computer or other external devices. They offer an extended degree of freedom either by strengthening or by substituting human peripheral working capacity and have potential applications in various fields such as rehabilitation, affective computing, robotics, gaming, and neuroscience. Significant research efforts on a global scale have delivered common platforms for technology standardization and help tackle highly complex and non-linear brain dynamics and related feature extraction and classification challenges. Time-variant psycho-neurophysiological fluctuations and their impact on brain signals impose another challenge for BCI researchers to transform the technology from laboratory experiments to plug-and-play daily life. This review summarizes state-of-the-art progress in the BCI field over the last decades and highlights critical challenges.

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Section: Challengesmentioning

confidence: 99%

Progress in Brain Computer Interface: Challenges and Opportunities

Saha

Mamun

Ahmed

et al. 2021

Front. Syst. Neurosci.

227

View full text Add to dashboard Cite

show abstract

“…Specifically, we put forward a line of reasoning promoting the reinstatement of subject learning as an equally important pillar in BMI training. We focus on non-invasive BMIs decoding sensorimotor rhythms (SMR) and invasive BMIs decoding kinematics, as the evidence supporting the possibility of learning to regulate evoked potentials is still limited [17].…”

Section: Introduction: On the Need For Mutual Learningmentioning

confidence: 99%

Brain-Machine Interfaces: A Tale of Two Learners

Perdikis

Millán

2020

IEEE Syst. Man Cybern. Mag.

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Brain-machine interface (BMI) technology has rapidly matured over the last two decades, mainly thanks to the introduction of artificial intelligence methods, in particular machine learning algorithms. Yet, the need for subjects to learn to modulate their brain activity is a key component of successful BMI control. Blending machine and subject learning, or mutual learning, is widely acknowledged in the BMI field. Nevertheless, we posit that current research trends are heavily biased towards the machine learning side of BMI training. Here, we take a critical view of the relevant literature and our own previous work, in order to identify key issues for more effective mutual learning schemes in translational BMIs, specifically tailored to promote subject learning. We identify the main caveats in the literature on subject learning in BMI, in particular lack of longitudinal studies involving end-users and shortcomings in quantifying subject learning, and pinpoint critical improvements for future experimental designs.

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“…Because of this limitation of the prevalent visual BCIs, many paradigms based on the auditory or tactile modalities have been developed. Still, the issue remains that non-visual BCIs are more challenging and workload-intensive than visual paradigms 17 , often resulting in lower BCI performances 18 – 20 .…”

Section: Introductionmentioning

confidence: 99%

Identifying potential training factors in a vibrotactile P300-BCI

Eidel

Kübler

2022

Sci Rep

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Brain–computer interfaces (BCI) often rely on visual stimulation and feedback. Potential end-users with impaired vision, however, cannot use these BCIs efficiently and require a non-visual alternative. Both auditory and tactile paradigms have been developed but are often not sufficiently fast or accurate. Thus, it is particularly relevant to investigate if and how users can train and improve performance. We report data from 29 healthy participants who trained with a 4-choice tactile P300-BCI during five sessions. To identify potential training factors, we pre-post assessed the robustness of the BCI performance against increased workload in a dual task condition and determined the participants’ somatosensory sensitivity thresholds with a forced-choice intensity discrimination task. Accuracy (M = 79.2% to 92.0%) and tactually evoked P300 amplitudes increased significantly, confirming successful training. Pre-post somatosensory sensitivity increased, and workload decreased significantly, but results of the dual task condition remained inconclusive. The present study confirmed the previously reported feasibility and trainability of our tactile BCI paradigm within a multi-session design. Importantly, we provide first evidence of improvement in the somatosensory system as a potential mediator for the observed training effects.

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Neural mechanisms of training an auditory event‐related potential task in a brain–computer interface context

Cited by 20 publications

References 76 publications

Progress in Brain Computer Interface: Challenges and Opportunities

Progress in Brain Computer Interface: Challenges and Opportunities

Brain-Machine Interfaces: A Tale of Two Learners

Identifying potential training factors in a vibrotactile P300-BCI

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