Wheelchair control by elderly participants in a virtual environment with a brain-computer interface (BCI) and tactile stimulation

Herweg, Andreas; Gutzeit, Julian; Kleih, Sonja C.; Kübler, Andrea

doi:10.1016/j.biopsycho.2016.10.006

Cited by 71 publications

(87 citation statements)

References 33 publications

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“…In recent years several paradigms were proposed that depend on auditory or tactile stimulation (Höhne et al, 2011; Schreuder et al, 2011; Kaufmann et al, 2013a; Hill et al, 2014; Käthner et al, 2015a; Kleih et al, 2015; Simon et al, 2015; Halder et al, 2016; Herweg et al, 2016). These paradigms could prove valuable for persons with most severe paralyses.…”

Section: Discussionmentioning

confidence: 99%

“…These paradigms could prove valuable for persons with most severe paralyses. Recently, Halder et al (2016) and Herweg et al (2016) demonstrated that potential end users could gain control over an auditory spelling application with training and control a tactile virtual wheelchair. Because these systems are still in the proof of concept stage, we did not implement them in the present prototype, but they should be considered for future systems.…”

Section: Discussionmentioning

confidence: 99%

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A Multifunctional Brain-Computer Interface Intended for Home Use: An Evaluation with Healthy Participants and Potential End Users with Dry and Gel-Based Electrodes

et al. 2017

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Current brain-computer interface (BCIs) software is often tailored to the needs of scientists and technicians and therefore complex to allow for versatile use. To facilitate home use of BCIs a multifunctional P300 BCI with a graphical user interface intended for non-expert set-up and control was designed and implemented. The system includes applications for spelling, web access, entertainment, artistic expression and environmental control. In addition to new software, it also includes new hardware for the recording of electroencephalogram (EEG) signals. The EEG system consists of a small and wireless amplifier attached to a cap that can be equipped with gel-based or dry contact electrodes. The system was systematically evaluated with a healthy sample, and targeted end users of BCI technology, i.e., people with a varying degree of motor impairment tested the BCI in a series of individual case studies. Usability was assessed in terms of effectiveness, efficiency and satisfaction. Feedback of users was gathered with structured questionnaires. Two groups of healthy participants completed an experimental protocol with the gel-based and the dry contact electrodes (N = 10 each). The results demonstrated that all healthy participants gained control over the system and achieved satisfactory to high accuracies with both gel-based and dry electrodes (average error rates of 6 and 13%). Average satisfaction ratings were high, but certain aspects of the system such as the wearing comfort of the dry electrodes and design of the cap, and speed (in both groups) were criticized by some participants. Six potential end users tested the system during supervised sessions. The achieved accuracies varied greatly from no control to high control with accuracies comparable to that of healthy volunteers. Satisfaction ratings of the two end-users that gained control of the system were lower as compared to healthy participants. The advantages and disadvantages of the BCI and its applications are discussed and suggestions are presented for improvements to pave the way for user friendly BCIs intended to be used as assistive technology by persons with severe paralysis.

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Section: Discussionmentioning

confidence: 99%

Section: Discussionmentioning

confidence: 99%

A Multifunctional Brain-Computer Interface Intended for Home Use: An Evaluation with Healthy Participants and Potential End Users with Dry and Gel-Based Electrodes

et al. 2017

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“…ese processes seek to identify the intention of the user in order to generate corresponding commands. Finally, these commands can be used for practical applications including, but not limited to, wheelchair navigation [4,5], character speller [6,7], and robotic arm manipulation [8,9]. e brain activities that are most frequently used to control BCI systems include event-related potentials (ERP) [10], steady-state evoked potentials [11], and event-related desynchronization (ERD) [12] and event-related synchronization (ERS) [13].…”

Section: Introductionmentioning

confidence: 99%

Effects of Visual Attention on Tactile P300 BCI

Chen

Jin

Daly

et al. 2020

Computational Intelligence and Neuroscience

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Objective. Tactile P300 brain-computer interfaces (BCIs) can be manipulated by users who only need to focus their attention on a single-target stimulus within a stream of tactile stimuli. To date, a multitude of tactile P300 BCIs have been proposed. In this study, our main purpose is to explore and investigate the effects of visual attention on a tactile P300 BCI. Approach. We designed a conventional tactile P300 BCI where vibration stimuli were provided by five stimulators and two of them were fixed on target locations on the participant's left and right wrists. Two conditions (one condition with visual attention and the other condition without visual attention) were tested by eleven healthy participants. Main Results. Our results showed that, when participants visually attended to the location of target stimulus, significantly higher classification accuracies and information transfer rates were obtained (both for p < 0.05). Furthermore, participants reported that visually attending to the stimulus made it easier to identify the target stimulus in random sequences of vibration stimuli. Significance. ese findings suggest that visual attention has positive effects on both tactile P300 BCI performance and user-evaluation.

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“…Although the training is able to increase severity of EEG features and makes the recognition process easier for ANN-based algorithms [20], it cannot be effectively used for patients with motor and mental disabilities [16,21]. Therefore, the creation of a universal BCI enable to work with untrained subjects would be useful for motor rehabilitation of such patients.…”

Section: Introductionmentioning

confidence: 99%

“…Then, we reduce the number of the EEG channels and obtain an appropriate recognition quality (up to 73 ± 15%) using only 8 electrodes located in frontal lobe. Finally, we analyze the time-frequency structure of EEG signals and find that motor-related features associated with left and right leg motor imagery are more pronounced in the mu (8-13 Hz) and delta (1-5 Hz) brainwaves than in the high-frequency beta brainwave (15)(16)(17)(18)(19)(20)(21)(22)(23)(24)(25)(26)(27)(28)(29)(30). Based on the obtained results, we propose further ANN optimization by preprocessing the EEG signals with a low-pass filter with different cutoffs.…”

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confidence: 99%

Artificial Neural Network Classification of Motor‐Related EEG: An Increase in Classification Accuracy by Reducing Signal Complexity

et al. 2018

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We apply artificial neural network (ANN) for recognition and classification of electroencephalographic (EEG) patterns associated with motor imagery in untrained subjects. Classification accuracy is optimized by reducing complexity of input experimental data. From multichannel EEG recorded by the set of 31 electrodes arranged according to extended international 10-10 system, we select an appropriate type of ANN which reaches 80 ± 10% accuracy for single trial classification. Then, we reduce the number of the EEG channels and obtain an appropriate recognition quality (up to 73 ± 15%) using only 8 electrodes located in frontal lobe. Finally, we analyze the time-frequency structure of EEG signals and find that motor-related features associated with left and right leg motor imagery are more pronounced in the mu (8-13 Hz) and delta (1-5 Hz) brainwaves than in the high-frequency beta brainwave (15)(16)(17)(18)(19)(20)(21)(22)(23)(24)(25)(26)(27)(28)(29)(30). Based on the obtained results, we propose further ANN optimization by preprocessing the EEG signals with a low-pass filter with different cutoffs. We demonstrate that the filtration of high-frequency spectral components significantly enhances the classification performance (up to 90 ± 5% accuracy using 8 electrodes only). The obtained results are of particular interest for the development of brain-computer interfaces for untrained subjects.

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Wheelchair control by elderly participants in a virtual environment with a brain-computer interface (BCI) and tactile stimulation

Cited by 71 publications

References 33 publications

A Multifunctional Brain-Computer Interface Intended for Home Use: An Evaluation with Healthy Participants and Potential End Users with Dry and Gel-Based Electrodes

A Multifunctional Brain-Computer Interface Intended for Home Use: An Evaluation with Healthy Participants and Potential End Users with Dry and Gel-Based Electrodes

Effects of Visual Attention on Tactile P300 BCI

Artificial Neural Network Classification of Motor‐Related EEG: An Increase in Classification Accuracy by Reducing Signal Complexity

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