EEG error-related potentials encode magnitude of errors and individual perceptual thresholds

Iwane, Fumiaki; Sobolewski, A.; Chavarriaga, Ricardo; Millán, José del R.

doi:10.1016/j.isci.2023.107524

Cited by 7 publications

(11 citation statements)

References 76 publications

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“…Although a previous work showed the customization of robot trajectories through continuous ErrP classification [25], their task did not involve varying magnitudes in erroneous interactions. A recent study [24] revealed the scalability of the ErrP-BCI decoding outputs over the magnitude of errors, and posed the challenge of decoding ErrPs induced by small errors. Critically, our experimental protocol did not include trials with a small magnitude of distortions in the decodercalibration phase.…”

Section: Discussionmentioning

confidence: 99%

“…The CCA spatial filter transformed the averaged ErrPs into a subspace that contained different deflections. Only the first three components were kept for further processing as described in previous studies [24,36]. We extracted EEG amplitudes resampled at 64 Hz and Welch's power spectral density between [4,10] Hz with a step of 2 Hz as they have been shown to yield superior performance in other studies [24,25,40].…”

Section: Time-locked Classification Of Errpsmentioning

confidence: 99%

“…Only the first three components were kept for further processing as described in previous studies [24,36]. We extracted EEG amplitudes resampled at 64 Hz and Welch's power spectral density between [4,10] Hz with a step of 2 Hz as they have been shown to yield superior performance in other studies [24,25,40]. All computed features were concatenated and normalized within the range of [0, 1] via Min-Max normalization.…”

Section: Time-locked Classification Of Errpsmentioning

confidence: 99%

“…It is also well established that brain-computer interfaces (BCIs) benefit from real-time ErrP detection to offer intuitive control of external devices without requiring explicit feedback, as instead they can infer participants' perception of errors from their brain activity and adapt accordingly [17][18][19][20][21]. Some BCIs have succeeded in decoding the presence of ErrPs during continuous interaction [22][23][24], with e.g. the possibility to customize robot trajectories for each participant based on continuous ErrP detection [25,26].…”

Section: Introductionmentioning

confidence: 99%

“…Although recent studies show that ErrP-based BCI allows customization of continuous humancomputer interactions, its use is still limited to interactions with a computer application [24] or a robotic arm [25,26]. This may in part be due to the need to train personalized decoders, which require a large amount of repetitions and observations before being operational.…”

Section: Introductionmentioning

confidence: 99%

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Customizing the human-avatar mapping based on EEG error related potentials

Iwane,

Porssut,

Blanke

et al. 2024

J. Neural Eng.

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Objective. A key challenge of virtual reality (VR) applications is to maintain a reliable human-avatar mapping. Users may lose the sense of controlling (sense of agency), owning (sense of body ownership), or being located (sense of self-location) inside the virtual body when they perceive erroneous interaction, i.e. Break-in-embodiment (BiE). However, the way to detect such an inadequate event is currently limited to questionnaires or spontaneous reports from users. The ability to implicitly detect BiE in real-time enables us to adjust human-avatar mapping without interruption.Approach. We propose and empirically demonstrate a novel Brain Computer Interface (BCI) approach that monitors the occurrence of BiE based on the users' brain oscillatory activity in real-time to adjust the human-avatar mapping in VR. We collected EEG data of 37 participants while they performed reaching movements with their avatar with different magnitude of distortion. Main results. Our BCI approach seamlessly predicts occurrence of BiE in varying magnitude of erroneous interaction. The mapping has been customized by BCI-reinforcement learning (RL) closed-loop system to prevent BiE from occurring. Furthermore, a non-personalized BCI decoder generalizes to new users, enabling "Plug-and-Play" ErrP-based non-invasive BCI. The proposed VR system allows customization of human-avatar mapping without personalized BCI decoders or spontaneous reports. Significance. We anticipate that our newly developed VR-BCI can be useful to maintain an engaging avatar-based interaction and a compelling immersive experience while detecting when users notice a problem and seamlessly correcting it.

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Section: Discussionmentioning

confidence: 99%

Section: Time-locked Classification Of Errpsmentioning

confidence: 99%

Section: Time-locked Classification Of Errpsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Customizing the human-avatar mapping based on EEG error related potentials

Iwane,

Porssut,

Blanke

et al. 2024

J. Neural Eng.

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Neural interfaces: Bridging the brain to the world beyond healthcare

Xu,

Liu,

Lee

et al. 2024

Exploration

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Neural interfaces, emerging at the intersection of neurotechnology and urban planning, promise to transform how we interact with our surroundings and communicate. By recording and decoding neural signals, these interfaces facilitate direct connections between the brain and external devices, enabling seamless information exchange and shared experiences. Nevertheless, their development is challenged by complexities in materials science, electrochemistry, and algorithmic design. Electrophysiological crosstalk and the mismatch between electrode rigidity and tissue flexibility further complicate signal fidelity and biocompatibility. Recent closed‐loop brain‐computer interfaces, while promising for mood regulation and cognitive enhancement, are limited by decoding accuracy and the adaptability of user interfaces. This perspective outlines these challenges and discusses the progress in neural interfaces, contrasting non‐invasive and invasive approaches, and explores the dynamics between stimulation and direct interfacing. Emphasis is placed on applications beyond healthcare, highlighting the need for implantable interfaces with high‐resolution recording and stimulation capabilities.

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