Fumiaki Iwane

EEG signature of breaks in embodiment in VR

Olaf Blanke, Ronan Boulic, Ricardo Andres Chavarriaga Lozano, Bruno Herbelin, Fumiaki Iwane

The brain mechanism of embodiment in a virtual body has grown a scientific interest recently, with a particular focus on providing optimal virtual reality (VR) experiences. Disruptions from an embodied state to a less- or non-embodied state, denominated Breaks in Embodiment (BiE), are however rarely studied despite their importance for designing interactions in VR. Here we use electroencephalography (EEG) to monitor the brain’s reaction to a BiE, and investigate how this reaction depends on previous embodiment conditions. The experimental protocol consisted of two sequential steps; an induction step where participants were either embodied or non-embodied in an avatar, and a monitoring step where, in some cases, participants saw the avatar’s hand move while their hand remained still. Our results show the occurrence of error-related potentials linked to observation of the BiE event in the monitoring step. Importantly, this EEG signature shows amplified potentials following the non-embodied condition, which is indicative of an accumulation of errors across steps. These results provide neurophysiological indications on how progressive disruptions impact the expectation of embodiment for a virtual body.

2023

Decoding of error-related potentials in continuous feedback protocols for personalized human computer interaction

Fumiaki Iwane

The ability to notice erroneous behavior is crucial for effective training. Within the framework of neuroprosthetics, numerous studies in electroencephalography (EEG) confirm the existence of neural correlates when humans perceive the erroneous actions of the device. Subsequently, the decoding of this correlate has been used to correct the erroneous behavior performed by the agent or to tune the behavioral strategy of the agent, among others. However, a main limitation of current approaches is that the actions of the agent were discretized, thus restraining the usability of such systems. The main objective of this PhD study is to study, and decode, the neural correlates of error evaluation under continuous trajectories performed by external agents; and to use this decoding to tune the continuous behavior of the agent for individual users. To accomplish this goal, two essential questions will be investigated: (i) whether it is possible to infer individual preference under continuous state-action scenarios, and (ii) how to create a reliable decoding pipeline in a continuous fashion. Results obtained during the first year of the PhD have confirmed the existence of such correlates under continuous motions of a robotic arm. Furthermore, such correlates encode individual preferences, indicating that the neural prosthesis can be also customized for individual users, which may play an important rule to increase the quality of brain-computer based assistance. This property not only will increase the level of perceived assistance provided by a brain-computer interface, but also may facilitate embodiment of the brain-controlled device.

EPFL2021

Inferring subjective preferences on robot trajectories using EEG signals

Iason Batzianoulis, Aude Billard, Ricardo Andres Chavarriaga Lozano, Inaki Asier Iturrate Gil, Fumiaki Iwane

Cognitive information has been exploited in non-invasive Brain Computer Interface (BCI) scenarios to provide autonomous external agents with additional information. In this context, Error-related potentials (ErrPs), temporal deflections in electroencephalogram (EEG) signals when humans perceive erroneous actions, have been exploited to teach correct policies to agents. However, previous works have shared same objective criteria to evaluate actions of agents across humans. Therefore, it is yet an open question whether ErrPs are elicited when humans assess actions based on individual subjective criteria, and if such neuronal activities can be exploited in BCIs to enhance personalized human computer interactions. In this work, we evaluate whether ErrPs are elicited while humans assess actions based on individual subjective criteria. For this purpose, we analyze EEG signals while humans evaluate trajectories performed by the robot to avoid an obstacle. We show that the ErrP can be generated even while the human evaluates the action based on his/ her subjective criteria, and that such neuronal activity reveals subjective aspects when assessing the appropriateness of the action. These findings open the door to the exploitation of neural correlates for the personalized human computer interactions, and thus to building adaptable, individualized neuroprosthetic devices.

IEEE2019

EEG-based lower-limb movement onset decoding: Continuous classification and asynchronous detection

Mohamed Bouri, Ricardo Andres Chavarriaga Lozano, Fumiaki Iwane, Kyuhwa Lee, Dong Liu

Brain-machine interfaces (BMIs) have been used to incorporate the user intention to trigger robotic devices by decoding movement onset from electroencephalography (EEG). Active neural participation is crucial to promote brain plasticity thus to enhance the opportunity of motor recovery. This study presents the decoding of lower-limb movement-related cortical potentials (MRCPs) with continuous classification and asynchronous detection.We executed experiments in a customized gait trainer where 10 healthy subjects performed self-initiated ankle plantar flexion. We further analyzed the features, evaluated the impact of the limb side, and compared the proposed framework with other typical decoding methods. No significant differences were observed between left and right legs in terms of neural signatures of movement and classification performance. We obtained higher true positive rate (TPR), lower false positives, and comparable latencies with respect to the existing online detection methods. This paper demonstrates the feasibility of the proposed framework to build a closed-loop gait trainer. Potential applications include gait training neurorehabilitation in clinical trials.

2018

Spatial filters yield stable features for error-related potentials across conditions

Ricardo Andres Chavarriaga Lozano, Inaki Asier Iturrate Gil, Fumiaki Iwane

Error-related potentials (ErrP) have been increasingly studied in psychophysical experiments as well as for brain-machine interfacing. In the latter case, the generalisation capabilities of ErrP decoders is a crucial element to avoid frequent recalibration processes, thus increasing their usability. Previous studies have suggested that ErrP signals are rather stable across recording sessions. Also, studies using protocols of serial stimuli presentation show that these potentials do not change significantly with the presentation rate. Here we complement these studies by analysing the decoding generalisation capabilities. Using data from monitoring experiments, we evaluate how much the performance degrades when tested in a condition different than the one the decoder was trained with. Moreover, we compare different spatial filtering techniques to see which preprocessing steps yield less-sensitive features for ErrP decoding.

2016