Visual and haptic feedback in detecting motor imagery within a wearable brain–computer interface

Arpaïa, Pasquale; Coyle, Damien; Rigoli, Francesco; Espósito, Antonio; Natalizio, Angela; Parvis, Marco

doi:10.1016/j.measurement.2022.112304

Cited by 8 publications

(15 citation statements)

References 51 publications

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“…Tactile interfaces can be useful for individuals who are visually or auditory impaired, in situations where visual or auditory information is overwhelming or distracting and in tasks that require spatial awareness, such as navigation or manipulation of objects [47][48][49]. Tactile interfaces can be more private than visual or auditory interfaces, can be concealed and wearable [50]. From the perspective of BCI application in the domain of neurofeedback and neuromodulation, the tactile interfaces may be applicable for sensorimotor neuromodulation to induce neuroplasticity, since the BCI control modality directly interacts with the sensorimotor network [13].…”

Section: Prospects For Tactile Bci Applicationsmentioning

confidence: 99%

Somatosensory Event-Related Potential as an Electrophysiological Correlate of Endogenous Spatial Tactile Attention: Prospects for Electrotactile Brain-Computer Interface for Sensory Training

Novičić

Savić

2023

Brain Sciences

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Tactile attention tasks are used in the diagnosis and treatment of neurological and sensory processing disorders, while somatosensory event-related potentials (ERP) measured by electroencephalography (EEG) are used as neural correlates of attention processes. Brain-computer interface (BCI) technology provides an opportunity for the training of mental task execution via providing online feedback based on ERP measures. Our recent work introduced a novel electrotactile BCI for sensory training, based on somatosensory ERP; however, no previous studies have addressed specific somatosensory ERP morphological features as measures of sustained endogenous spatial tactile attention in the context of BCI control. Here we show the morphology of somatosensory ERP responses induced by a novel task introduced within our electrotactile BCI platform i.e., the sustained endogenous spatial electrotactile attention task. By applying pulsed electrical stimuli to the two proximal stimulation hotspots at the user’s forearm, stimulating sequentially the mixed branches of radial and median nerves with equal probability of stimuli occurrence, we successfully recorded somatosensory ERPs for both stimulation locations, in the attended and unattended conditions. Waveforms of somatosensory ERP responses for both mixed nerve branches showed similar morphology in line with previous reports on somatosensory ERP components obtained by stimulation of exclusively sensory nerves. Moreover, we found statistically significant increases in ERP amplitude on several components, at both stimulation hotspots, while sustained endogenous spatial electrotactile attention task is performed. Our results revealed the existence of general ERP windows of interest and signal features that can be used to detect sustained endogenous tactile attention and classify between spatial attention locations in 11 healthy subjects. The current results show that features of N140, P3a and P3b somatosensory ERP components are the most prominent global markers of sustained spatial electrotactile attention, over all subjects, within our novel electrotactile BCI task/paradigm, and this work proposes the features of those components as markers of sustained endogenous spatial tactile attention in online BCI control. Immediate implications of this work are the possible improvement of online BCI control within our novel electrotactile BCI system, while these finding can be used for other tactile BCI applications in the diagnosis and treatment of neurological disorders by employing mixed nerve somatosensory ERPs and sustained endogenous electrotactile attention task as control paradigms.

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Section: Prospects For Tactile Bci Applicationsmentioning

confidence: 99%

Somatosensory Event-Related Potential as an Electrophysiological Correlate of Endogenous Spatial Tactile Attention: Prospects for Electrotactile Brain-Computer Interface for Sensory Training

Novičić

Savić

2023

Brain Sciences

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“…Additionally, another study used the same haptic vest as in this research as a feedback modality during motor imagery. In this case, too, no EEG analysis was presented, with the focus being solely on motor imagery classification 32 .…”

Section: Discussionmentioning

confidence: 99%

“…As such, it is difficult to distinguish whether the emotions produced were due to haptic, visual or auditory stimulation. There have also been studies that have used upper body vibration stimulation as feedback to enhance motor imagery 32 ; however, these studies used upper body vibration as feedback and did not examine the EEG response to the vibration stimulus itself.…”

Section: Introductionmentioning

confidence: 99%

EEG correlates to perceived urgency elicited by vibration stimulation of the upper body

Park,

Alsuradi,

Eid

2024

Sci Rep

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Conveying information effectively while minimizing user distraction is critical to human–computer interaction. As the proliferation of audio–visual communication pushes human information processing capabilities to the limit, researchers are turning their attention to haptic interfaces. Haptic feedback has the potential to create a desirable sense of urgency that allows users to selectively focus on events/tasks or process presented information with minimal distraction or annoyance. There is a growing interest in understanding the neural mechanisms associated with haptic stimulation. In this study, we aim to investigate the EEG correlates associated with the perceived urgency elicited by vibration stimuli on the upper body using a haptic vest. A total of 31 participants enrolled in this experiment and were exposed to three conditions: no vibration pattern (NVP), urgent vibration pattern (UVP), and very urgent vibration pattern (VUVP). Through self-reporting, participants confirmed that the vibration patterns elicited significantly different levels of perceived urgency (Friedman test, Holm–Bonferroni correction, p < 0.01). Furthermore, neural analysis revealed that the power spectral density of the delta, theta, and alpha frequency bands in the middle central area (C1, Cz, and C2) significantly increased for the UVP and VUVP conditions as compared to the NVP condition (One-way ANOVA test, Holm–Bonferroni correction, p < 0.01). While the perceptual experience of haptic-induced urgency is well studied with self-reporting and behavioral evidence, this is the first effort to evaluate the neural correlates to haptic-induced urgency using EEG. Further research is warranted to identify unique correlates to the cognitive processes associated with urgency from sensory feedback correlates.

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“…Among the different ways of decoding brain activity, Electroencephalography (EEG) is receiving a strong interest by the scientific community since it is non-invasive, cheap, and is endowed with high temporal resolution to allow real-time operation [3], [4]. In fact, different EEG-based BCI paradigms, such as P300 [5] and Motor Imagery [6]- [10] have already been successfully employed in several contexts but, in particular, Steady-State Visually Evoked Potentials (SSVEPs) have gained outstanding relevance for the development of applications in healthcare, [11], [12] entertainment [13], and industry [14], [15] owing to quick response, easy detection, high signalto-noise ratio (SNR) [16]. As a matter of fact, the classification of SSVEPs can be performed with good results even with simple, trainingless algorithms, such as Power Spectral Density Analysis (PSDA) or Canonical Correlation Analysis (CCA) [17].…”

Section: Introductionmentioning

confidence: 99%

Employment of Domain Adaptation Techniques in SSVEP-Based Brain–Computer Interfaces

et al. 2023

Self Cite

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This work addresses the employment of Machine Learning (ML) and Domain Adaptation (DA) in the framework of Brain-Computer Interfaces (BCIs) based on Steady-State Visually Evoked Potentials (SSVEPs). Currently, all the state-of-the-art classification strategies do not consider the high non-stationarity typical of brain signals. This can lead to poor performance, expecially when short-time signals have to be considered to allow real-time human-environment interaction. In this regard, ML and DA techniques can represent a suitable strategy to enhance the performance of SSVEPs classification pipelines. In particular, the employment of a two-step DA technique is proposed: first, the standardization of the data per subject is performed by exploiting a part of unlabeled test data during the training stage; second, a similarity measure between subjects is considered in the selection of the validation sets. The proposal was applied to three classifiers to verify the statistical significance of the improvements over the standard approaches. These classifiers were validated and comparatively tested on a well-known public benchmark dataset. An appropriate validation method was used in order to simulate real-world usage. The experimental results show that the proposed approach significantly improves the classification accuracy of SSVEPs. In fact, up to 62.27 % accuracy was achieved also in the case of short-time signals (i.e., 1.0 s). This represents a further confirmation of the suitability of advanced ML to improve the performance of BCIs for daily-life applications.

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Visual and haptic feedback in detecting motor imagery within a wearable brain–computer interface

Cited by 8 publications

References 51 publications

Somatosensory Event-Related Potential as an Electrophysiological Correlate of Endogenous Spatial Tactile Attention: Prospects for Electrotactile Brain-Computer Interface for Sensory Training

Somatosensory Event-Related Potential as an Electrophysiological Correlate of Endogenous Spatial Tactile Attention: Prospects for Electrotactile Brain-Computer Interface for Sensory Training

EEG correlates to perceived urgency elicited by vibration stimulation of the upper body

Employment of Domain Adaptation Techniques in SSVEP-Based Brain–Computer Interfaces

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