An Evaluation of Training with an Auditory P300 Brain-Computer Interface for the Japanese Hiragana Syllabary

Halder, Sebastian; Takano, Kouji; Ora, Hiroki; Onishi, Akinari; Utsumi, Kota; Kansaku, Kenji

doi:10.3389/fnins.2016.00446

Cited by 18 publications

(18 citation statements)

References 49 publications

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“…Baykara and colleagues also observed a decrease in performance from the second to the third EEG training session but after the third session performance stabilised (Baykara et al, ). In a further study over three sessions with healthy participants, the strongest increase in ITR occurred between the first and second sessions (Halder, Takano, et al, ). In both studies as in the current one, most of the learning occurred between the first and second training sessions, which are compatible with Logan's instance theory of memory and learning (Logan, ).…”

Section: Discussionmentioning

confidence: 92%

Section: Eeg Bci Performancementioning

confidence: 87%

“…This difference can be easily and reliably detected by respective algorithms and was shown to be stable across years even in patients with amyotrophic lateral sclerosis (ALS) (Holz et al, ). Importantly, it has been shown repeatedly, in healthy volunteers and those with motor impairment alike, that training can improve performance in a non‐visual ERP‐based BCI (Baykara et al, ; Halder et al, ; Halder, Käthner, & Kübler, ). Although ERP paradigms are particularly convenient to restore communication for persons with severe paralysis (Nijboer et al, ), to date no study has examined the neural mechanisms of acquiring control in ERP‐based BCI.…”

Section: Introductionmentioning

confidence: 98%

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

Halder

Leinfelder

Schulz

et al. 2019

Human Brain Mapping

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Effective use of brain–computer interfaces (BCIs) typically requires training. Improved understanding of the neural mechanisms underlying BCI training will facilitate optimisation of BCIs. The current study examined the neural mechanisms related to training for electroencephalography (EEG)‐based communication with an auditory event‐related potential (ERP) BCI. Neural mechanisms of training in 10 healthy volunteers were assessed with functional magnetic resonance imaging (fMRI) during an auditory ERP‐based BCI task before (t1) and after (t5) three ERP‐BCI training sessions outside the fMRI scanner (t2, t3, and t4). Attended stimuli were contrasted with ignored stimuli in the first‐level fMRI data analysis (t1 and t5); the training effect was verified using the EEG data (t2‐t4); and brain activation was contrasted before and after training in the second‐level fMRI data analysis (t1 vs. t5). Training increased the communication speed from 2.9 bits/min (t2) to 4 bits/min (t4). Strong activation was found in the putamen, supplementary motor area (SMA), and superior temporal gyrus (STG) associated with attention to the stimuli. Training led to decreased activation in the superior frontal gyrus and stronger haemodynamic rebound in the STG and supramarginal gyrus. The neural mechanisms of ERP‐BCI training indicate improved stimulus perception and reduced mental workload. The ERP task used in the current study showed overlapping activations with a motor imagery based BCI task from a previous study on the neural mechanisms of BCI training in the SMA and putamen. This suggests commonalities between the neural mechanisms of training for both BCI paradigms.

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Section: Discussionmentioning

confidence: 92%

Section: Eeg Bci Performancementioning

confidence: 87%

Section: Introductionmentioning

confidence: 98%

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

Halder

Leinfelder

Schulz

et al. 2019

Human Brain Mapping

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“…These EEG signals were recorded with g.USBamp and g.EEGcap systems (Guger Technologies, Graz, Austria). The sample rate of the amplifier was set as 256 Hz, the sensitivity value was100µV, and a third-order Butterworth band-pass filter was applied from 0.1 to 30 Hz (Munssinger et al, 2010;Halder et al, 2016). In this paper, we chose 16 electrode positions, based on the international 10-20 system (Jin et al, 2014a), which were positioned over areas of the brain associated with vision.…”

Section: Electroencephalogram Acquisitionmentioning

confidence: 99%

Comparison of the ERP-Based BCI Performance Among Chromatic (RGB) Semitransparent Face Patterns

Jin

Daly

et al. 2020

Front. Neurosci.

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Objective: Previous studies have shown that combing with color properties may be used as part of the display presented to BCI users in order to improve performance. Build on this, we explored the effects of combinations of face stimuli with three primary colors (RGB) on BCI performance which is assessed by classification accuracy and information transfer rate (ITR). Furthermore, we analyzed the waveforms of three patterns. Methods:We compared three patterns in which semitransparent face is overlaid three primary colors as stimuli: red semitransparent face (RSF), green semitransparent face (GSF), and blue semitransparent face (BSF). Bayesian linear discriminant analysis (BLDA) was used to construct the individual classifier model. In addition, a Repeated-measures ANOVA (RM-ANOVA) and Bonferroni correction were chosen for statistical analysis. Results:The results indicated that the RSF pattern achieved the highest online averaged accuracy with 93.89%, followed by the GSF pattern with 87.78%, while the lowest performance was caused by the BSF pattern with an accuracy of 81.39%. Furthermore, significant differences in classification accuracy and ITR were found between RSF and GSF (p < 0.05) and between RSF and BSF patterns (p < 0.05). Conclusion:The semitransparent faces colored red (RSF) pattern yielded the best performance of the three patterns. The proposed patterns based on ERP-BCI system have a clinically significant impact by increasing communication speed and accuracy of the P300-speller for patients with severe motor impairment.

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“…In the last decade, machine-learning methods have been applied to test whether the target of selective attention can be detected from electro- and magnetoencephalographic (EEG/MEG) data (Nijboer et al, 2008; Furdea et al, 2009; Halder et al, 2010, 2016; Schreuder et al, 2010; Höhne et al, 2011; Hill et al, 2012; Nambu et al, 2013; Hübner et al, 2018). EEG and MEG are well suited for monitoring attention effects as they provide a high temporal resolution in the order of milliseconds, enabling the detection and classification of evoked responses (e.g.…”

Section: Introductionmentioning

confidence: 99%

Target of selective auditory attention can be robustly followed with MEG

Kurmanavičiūtė

Rantala

Jas

et al. 2019

Preprint

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Selective auditory attention enables filtering relevant from irrelevant acoustic information. Specific auditory responses, measurable by electro-and magnetoencephalography (EEG/MEG), are known to be modulated by attention to the evoking stimuli. However, these attention effects are typically demonstrated in averaged responses and their robustness in single trials is not studied extensively.We applied decoding algorithms to MEG to investigate how well the target of auditory attention could be determined from single responses and which spatial and temporal aspects of the responses carry most of the information regarding the target of attention. To this end, we recorded brain responses of 15 healthy subjects with MEG when they selectively attended to one of the simultaneously presented auditory streams of words "Yes" and "No". A support vector machine was trained on the MEG data both at the sensor and source level to predict at every trial which stream was attended.Sensor-level decoding of the attended stream using the entire 2-s epoch resulted in a mean accuracy of 93% ± 1% (range 83-99% across subjects). Time-resolved decoding revealed that the highest accuracies were obtained 200-350 ms after the stimulus onset. Spatially-resolved source-level decoding indicated that the cortical sources most informative of the attended stream were located in the primary auditory cortex, especially in the right hemisphere.Our result corroborates attentional modulation of auditory evoked responses also to naturalistic stimuli. The achieved high decoding accuracy could enable the use of our experimental paradigm and classification method in a brain-computer interface.

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An Evaluation of Training with an Auditory P300 Brain-Computer Interface for the Japanese Hiragana Syllabary

Cited by 18 publications

References 49 publications

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

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

Comparison of the ERP-Based BCI Performance Among Chromatic (RGB) Semitransparent Face Patterns

Target of selective auditory attention can be robustly followed with MEG

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