Adding Tactile Feedback and Changing ISI to Improve BCI Systems’ Robustness: An Error-Related Potential Study

Ahkami, Bahareh; Ghassemi, Farnaz

doi:10.1007/s10548-021-00840-6

Cited by 13 publications

(7 citation statements)

References 18 publications

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“…In Ahkami and Ghassemi (2021), a visual or tactile stimulus was used as a cue to indicate the upcoming movements or the direction of upcoming movements. ErrPs were evoked during the recognition of (1) the incongruence between the cued movements and the absence of movements (i.e., errors) or (2) the incongruence between the cued movement direction and the direction of the actually executed movement (i.e., error).…”

Section: Related Workmentioning

confidence: 99%

“…In summary, visual and tactile stimuli were used together to evoke ErrPs (Tessadori et al, 2017;Schiatti et al, 2019) or tactile stimuli were used to indicate upcoming errors and the visual recognition of errors evoked ErrPs (Perrin et al, 2008;Chavarriaga et al, 2012;Ahkami and Ghassemi, 2021).…”

Section: Related Workmentioning

confidence: 99%

“…In other studies, tactile stimuli were used to indicate upcoming errors, but the errors were recognized visually. Thus, ErrPs were evoked by visual recognition of errors indicated by the tactile cue (Chavarriaga et al, 2012;Ahkami and Ghassemi, 2021). In Perrin et al (2008), various modalities of stimuli (visual, auditory, tactile) were compared, in which the effect of different modalities of stimuli on reaction time was systematically investigated, but only preliminary results of ErrP classification were shown due to the very small sample size and very large variability between subjects and between stimulus types.…”

Section: Introductionmentioning

confidence: 99%

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Detection of tactile-based error-related potentials (ErrPs) in human-robot interaction

Kim,

Kirchner

2023

Front. Neurorobot.

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Robot learning based on implicitly extracted error detections (e.g., EEG-based error detections) has been well-investigated in human-robot interaction (HRI). In particular, the use of error-related potential (ErrP) evoked when recognizing errors is advantageous for robot learning when evaluation criteria cannot be explicitly defined, e.g., due to the complex behavior of robots. In most studies, erroneous behavior of robots were recognized visually. In some studies, visuo-tactile stimuli were used to evoke ErrPs or a tactile cue was used to indicate upcoming errors. To our knowledge, there are no studies in which ErrPs are evoked when recognizing errors only via the tactile channel. Hence, we investigated ErrPs evoked by tactile recognition of errors during HRI. In our scenario, subjects recognized errors caused by incorrect behavior of an orthosis during the execution of arm movements tactilely. EEG data from eight subjects was recorded. Subjects were asked to give a motor response to ensure error detection. Latency between the occurrence of errors and the response to errors was expected to be short. We assumed that the motor related brain activity is timely correlated with the ErrP and might be used from the classifier. To better interpret and test our results, we therefore tested ErrP detections in two additional scenarios, i.e., without motor response and with delayed motor response. In addition, we transferred three scenarios (motor response, no motor response, delayed motor response). Response times to error was short. However, high ErrP-classification performance was found for all subjects in case of motor response and no motor response condition. Further, ErrP classification performance was reduced for the transfer between motor response and delayed motor response, but not for the transfer between motor response and no motor response. We have shown that tactilely induced errors can be detected with high accuracy from brain activity. Our preliminary results suggest that also in tactile ErrPs the brain response is clear enough such that motor response is not relevant for classification. However, in future work, we will more systematically investigate tactile-based ErrP classification.

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Section: Related Workmentioning

confidence: 99%

Section: Related Workmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Detection of tactile-based error-related potentials (ErrPs) in human-robot interaction

Kim,

Kirchner

2023

Front. Neurorobot.

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“…In both cases, results suggest that multimodal feedback was beneficial for motor imagery detection, even in comparison with a unimodal feedback modality. Meanwhile, the authors in [28] demonstrated that 65 better detection accuracy 1 was associated with visual or multimodal feedback if compared to the vibrotactile feedback alone. In that case, haptic feedback was provided by two vibrating motors placed on the wrists.…”

Section: Introductionmentioning

confidence: 99%

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

et al. 2023

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“…Low transfer rates and reliability due to errors are major factors that disincentivize the use of BCI systems [10]. To improve MI-based BCI transfer rates, methods have been proposed to respond to errors using error-related potentials or negativity [11], [12], [13], [14], [15]. However, this approach provides only a temporary solution, instead of a resolution [9].…”

Section: Introductionmentioning

confidence: 99%

Improving Performance of Motor Imagery-Based Brain–Computer Interface in Poorly Performing Subjects Using a Hybrid-Imagery Method Utilizing Combined Motor and Somatosensory Activity

Park

Kim

2023

IEEE Trans. Neural Syst. Rehabil. Eng.

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The phenomena of brain-computer interfaceinefficiency in transfer rates and reliability can hinder development and use of brain-computer interface technology. This study aimed to enhance the classification performance of motor imagery-based brain-computer interface (three-class: left hand, right hand, and right foot) of poor performers using a hybrid-imagery approach that combined motor and somatosensory activity. Twenty healthy subjects participated in these experiments involving the following three paradigms: (1) Control-condition: motor imagery only, (2) Hybrid-condition I: combined motor and somatosensory stimuli (same stimulus: rough ball), and (3) Hybridcondition II: combined motor and somatosensory stimuli (different stimulus: hard and rough, soft and smooth, and hard and rough ball). The three paradigms for all participants, achieved an average accuracy of 63.60±21.62%, 71.25±19.53%, and 84.09±12.79% using the filter bank common spatial pattern algorithm (5-fold cross-validation), respectively. In the poor performance group, the Hybridcondition II paradigm achieved an accuracy of 81.82%, showing a significant increase of 38.86% and 21.04% in accuracy compared to the control-condition (42.96%) and Hybrid-condition I (60.78%), respectively. Conversely, the good performance group showed a pattern of increasing accuracy, with no significant difference between the three

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Adding Tactile Feedback and Changing ISI to Improve BCI Systems’ Robustness: An Error-Related Potential Study

Cited by 13 publications

References 18 publications

Detection of tactile-based error-related potentials (ErrPs) in human-robot interaction

Detection of tactile-based error-related potentials (ErrPs) in human-robot interaction

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

Improving Performance of Motor Imagery-Based Brain–Computer Interface in Poorly Performing Subjects Using a Hybrid-Imagery Method Utilizing Combined Motor and Somatosensory Activity

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