Neural Signatures of Interface Errors in Remote Agent Manipulation

Using event-related brain potentials to evaluate motor-auditory latencies in virtual reality

Actions in the real world have immediate sensory consequences. Mimicking these in digital environments is within reach, but technical constraints usually impose a certain latency (delay) between user actions and system responses. It is important to assess the impact of this latency on the users, ideally with measurement techniques that do not interfere with their digital experience. One such unobtrusive technique is electroencephalography (EEG), which can capture the users' brain activity associated with motor responses and sensory events by extracting event-related potentials (ERPs) from the continuous EEG recording. Here we exploit the fact that the amplitude of sensory ERP components (specifically, N1 and P2) reflects the degree to which the sensory event was perceived as an expected consequence of an own action (self-generation effect). Participants (N = 24) elicit auditory events in a virtual-reality (VR) setting by entering codes on virtual keypads to open doors. In a within-participant design, the delay between user input and sound presentation is manipulated across blocks. Occasionally, the virtual keypad is operated by a simulated robot instead, yielding a control condition with externally generated sounds. Results show that N1 (but not P2) amplitude is reduced for self-generated relative to externally generated sounds, and P2 (but not N1) amplitude is modulated by delay of sound presentation in a graded manner. This dissociation between N1 and P2 effects maps back to basic research on self-generation of sounds. We suggest P2 amplitude as a candidate read-out to assess the quality and immersiveness of digital environments with respect to system latency.

Brain computer interface to distinguish between self and other related errors in human agent collaboration

When a human and machine collaborate on a shared task, ambiguous events might occur that could be perceived as an error by the human partner. In such events, spontaneous error-related potentials (ErrPs) are evoked in the human brain. Knowing whom the human perceived as responsible for the error would help a machine in co-adaptation and shared control paradigms to better adapt to human preferences. Therefore, we ask whether self- and agent-related errors evoke different ErrPs. Eleven subjects participated in an electroencephalography human-agent collaboration experiment with a collaborative trajectory-following task on two collaboration levels, where movement errors occurred as trajectory deviations. Independently of the collaboration level, we observed a higher amplitude of the responses on the midline central Cz electrode for self-related errors compared to observed errors made by the agent. On average, Support Vector Machines classified self- and agent-related errors with 72.64% accuracy using subject-specific features. These results demonstrate that ErrPs can tell if a person relates an error to themselves or an external autonomous agent during collaboration. Thus, the collaborative machine will receive more informed feedback for the error attribution that allows appropriate error identification, a possibility for correction, and avoidance in future actions.

Single-Trial Classification of Error-Related Potentials in People with Motor Disabilities: A Study in Cerebral Palsy, Stroke, and Amputees

Brain-computer interface performance may be reduced over time, but adapting the classifier could reduce this problem. Error-related potentials (ErrPs) could label data for continuous adaptation. However, this has scarcely been investigated in populations with severe motor impairments. The aim of this study was to detect ErrPs from single-trial EEG in offline analysis in participants with cerebral palsy, an amputation, or stroke, and determine how much discriminative information different brain regions hold. Ten participants with cerebral palsy, eight with an amputation, and 25 with a stroke attempted to perform 300-400 wrist and ankle movements while a sham BCI provided feedback on their performance for eliciting ErrPs. Pre-processed EEG epochs were inputted in a multi-layer perceptron artificial neural network. Each brain region was used as input individually (Frontal, Central, Temporal Right, Temporal Left, Parietal, and Occipital), the combination of the Central region with each of the adjacent regions, and all regions combined. The Frontal and Central regions were most important, and adding additional regions only improved performance slightly. The average classification accuracies were 84 ± 4%, 87± 4%, and 85 ± 3% for cerebral palsy, amputation, and stroke participants. In conclusion, ErrPs can be detected in participants with motor impairments; this may have implications for developing adaptive BCIs or automatic error correction.

Measuring Task-Related Brain Activity With Event-Related Potentials in Dynamic Task Scenario With Immersive Virtual Reality Environment

Measurement of event-related potentials (ERPs) in simulated and real environments is advantageous for understanding cognition and behavior during practice of goal-directed activities. Recently, instead of using task-irrelevant “probe stimuli” to elicit ERPs, extraction of ERPs directly from events that occur in simulated and real environments has drawn increased attention. Among the previous ERP studies using immersive virtual reality, only a few cases elicited ERPs from task-related events in dynamic task settings. Furthermore, as far as we surveyed, there were no studies that examined the source of ERPs or correlation between ERPs and behavioral performance in 360-degree immersive virtual reality using head-mounted display. In this study, EEG signals were recorded from 16 participants while they were playing the first-person shooter game with immersive virtual reality environment. Error related negativity (ERN) and correct-(response)-related negativity (CRN) elicited by shooting-related events were successfully extracted. We found the ERN amplitudes to be correlated with the individual shooting performance. Interestingly, the main source of the ERN was the rostral anterior cingulate cortex (ACC), which is different from previous studies where the signal source was often estimated to be the more caudal part of ACC. The obtained results are expected to contribute to the evaluation of cognitive functions and behavioral performance by ERPs in a simulated environment.

Invariability of EEG error-related potentials during continuous feedback protocols elicited by erroneous actions at predicted or unpredicted states

Objective.When humans perceive an erroneous action, an EEG error-related potential (ErrP) is elicited as a neural response. ErrPs have been largely investigated in discrete feedback protocols, where actions are executed at discrete steps, to enable seamless brain-computer interaction. However, there are only a few studies that investigate ErrPs in continuous feedback protocols. The objective of the present study is to better understand the differences between two types of ErrPs elicited during continuous feedback protocols, where errors may occur either at predicted or unpredicted states. We hypothesize that ErrPs of the unpredicted state is associated with longer latency as it requires higher cognitive workload to evaluate actions compared to the predicted states.Approach.Participants monitored the trajectory of an autonomous cursor that occasionally made erroneous actions on its way to the target in two conditions, namely, predicted or unpredicted states. After characterizing the ErrP waveform elicited by erroneous actions in the two conditions, we performed single-trial decoding of ErrPs in both synchronous (i.e. time-locked to the onset of the erroneous action) and asynchronous manner. Furthermore, we explored the possibility to transfer decoders built with data of one of the conditions to the other condition.Main results.As hypothesized, erroneous actions at unpredicted states gave rise to ErrPs with higher latency than erroneous actions at predicted states, a correlate of higher cognitive effort in the former condition. Moreover, ErrP decoders trained in a given condition successfully transferred to the other condition with a slight loss of classification performance. This was the case for synchronous as well as asynchronous ErrP decoding, showing the invariability of ErrPs across conditions.Significance.These results advance the characterization of ErrPs during continuous feedback protocols, enlarging the potential use of ErrPs during natural operation of brain-controlled devices as it is not necessary to have different decoders for each kind of erroneous conditions.