Detection Of Error-related Potentials Research Articles

Analysis of Error Potentials generated by a lower limb exoskeleton feedback in a BMI for gait control.

Brain-machine interfaces (BMIs) based on motor imagery (MI) for controlling lower-limb exoskeletons during the gait have been gaining importance in the rehabilitation field. However, these MI-BMI are not as precise as they should. The detection of error related potentials (ErrP) as a self-tune parameter to prevent wrong commands could be an interesting approach to improve their performance. For this reason, in this investigation ErrP elicited by the movement of a lower-limb exoskeleton against subject's will is analyzed in the time, frequency and time-frequency domain and compared with the cases where the exoskeleton is correctly commanded by motor imagery (MI). The results of the ErrP study indicate that there is statistical significative evidence of a difference between the signals in the erroneous events and the success events. Thus, ErrP could be used to increase the accuracy of BMIs which commands exoskeletons.Clinical Relevance- This investigation has the purpose of improving brain-machine interfaces (BMIs) based on motor imagery (MI) by means of the detection of error potentials. This could promote the adoption of robotic exoskeletons commanded by BMIs in rehabilitation therapies.

Detection of Error-Related Potentials in Stroke Patients from EEG Using an Artificial Neural Network.

Error-related potentials (ErrPs) have been proposed as a means for improving brain–computer interface (BCI) performance by either correcting an incorrect action performed by the BCI or label data for continuous adaptation of the BCI to improve the performance. The latter approach could be relevant within stroke rehabilitation where BCI calibration time could be minimized by using a generalized classifier that is continuously being individualized throughout the rehabilitation session. This may be achieved if data are correctly labelled. Therefore, the aims of this study were: (1) classify single-trial ErrPs produced by individuals with stroke, (2) investigate test–retest reliability, and (3) compare different classifier calibration schemes with different classification methods (artificial neural network, ANN, and linear discriminant analysis, LDA) with waveform features as input for meaningful physiological interpretability. Twenty-five individuals with stroke operated a sham BCI on two separate days where they attempted to perform a movement after which they received feedback (error/correct) while continuous EEG was recorded. The EEG was divided into epochs: ErrPs and NonErrPs. The epochs were classified with a multi-layer perceptron ANN based on temporal features or the entire epoch. Additionally, the features were classified with shrinkage LDA. The features were waveforms of the ErrPs and NonErrPs from the sensorimotor cortex to improve the explainability and interpretation of the output of the classifiers. Three calibration schemes were tested: within-day, between-day, and across-participant. Using within-day calibration, 90% of the data were correctly classified with the entire epoch as input to the ANN; it decreased to 86% and 69% when using temporal features as input to ANN and LDA, respectively. There was poor test–retest reliability between the two days, and the other calibration schemes led to accuracies in the range of 63–72% with LDA performing the best. There was no association between the individuals’ impairment level and classification accuracies. The results show that ErrPs can be classified in individuals with stroke, but that user- and session-specific calibration is needed for optimal ErrP decoding with this approach. The use of ErrP/NonErrP waveform features makes it possible to have a physiological meaningful interpretation of the output of the classifiers. The results may have implications for labelling data continuously in BCIs for stroke rehabilitation and thus potentially improve the BCI performance.

Single-trial detection of EEG error-related potentials using modified power-law transformation

Error processing in the human brain is characterized by a distinct neural response that is elicited when the outcome of an event is not the same as expected. This response is known as error-related potential (ErrP). Detection of error-related potentials from a single-trial data can lead the existing Brain-Computer Interface (BCI) systems one step closer to autonomous system. The non-stationarity of the Electroencephalograph (EEG) signal along with poor spatial resolution and low signal-to-noise ratio make the task of detecting ErrP on a single-trial basis challenging. The objective of this work is to propose a novel ErrP detection method based on temporal domain features of ErrP, particularly, the standard deviation measure. The difference in the standard deviation between the correct and the erroneous trials is verified using a statistical test and this information is subsequently used for ErrP detection. In the proposed method, the EEG electrodes from the frontocentral region of the brain are ranked using standard deviation based ranking criterion and a few top-ranked electrodes will be considered for single-trial detection of ErrP. Then, a modified power-law based transformation is proposed to enhance the discriminability between the correct and error trials. The discriminability is enhanced by transforming the samples lying outside a predefined amplitude range according to power-law equation and the samples within the selected amplitude range are transformed linearly. The modified power-law transformation has two parameters K and γ. The parameter K defines the pre-defined amplitude range and γ is the power in the standard power-law equation. The proposed ErrP detection method is tested on a publicly available EEG database of 10 subjects. The proposed transformation increases the discriminability between the erroneous and correct trials and achieves the best trade-off between the sensitivity and the specificity for detecting ErrP on single-trial basis compared to existing methods. The average sensitivity and specificity achieved is 86.17±9.05% and 92.05±3.69% respectively. The higher sensitivity of detection erroneous trials can be used to faithfully correct the wrongly decoded signal in the EEG based BCI system.

Capsule Neural Network Based Error Correlation Potential Detection for EEG Topographies

At present, the detection of error-related potentials is useful for the application of real-time error instruction correction techniques in brain-machine interface online systems. This paper, however, proposes a strategy for error-correlation potential detection based on EEG topographies, which translate the sequence of EEG topographies over time into a spatial position relationship between the features contained in different pictures. As the capsule network incorporates relative position relationships between features, i.e., positional information, a high classification accuracy can be achieved with a small dataset. Experimental evaluation has shown that the proposed method yields significant performance improvements compared to conventional processing methods.

Online detection of error-related potentials in multi-class cognitive task-based BCIs

ABSTRACTOne method for improving the accuracy and hence the rate of communication of a brain–computer interface (BCI) is to automatically correct erroneous classifications by exploiting error-related potentials (ErrPs). The merit of such a correction scheme has been demonstrated in both active (e.g. motor imagery) and reactive (e.g. P300) BCIs. Here, we investigated the effect of ErrP-guided error correction in a three-class, active BCI based on cognitive rather than motor imagery tasks using electroencephalography (EEG). Ten able-bodied adults participated in three sessions of data collection. For each participant, a ternary BCI differentiated among idle state and two personally selected cognitive tasks (e.g. mental arithmetic, counting, word generation, and figure rotation). Real-time feedback of the BCI decision was displayed to the participant following each task. EEG data after feedback onset were used to detect ErrPs and correct the BCI’s output in the case of detected errors. ErrP-based error correction modestly but significantly improved the average online task classification accuracy (+7%) as well as the information transfer rate (+0.9 bits/min) of the ternary BCI across participants. Our findings support further study of ErrPs in active BCIs based on cognitive tasks.

Taking brain-computer-interfacing one step further: a portable, wireless system coupled with online linear discriminant analysis for the detection of error-related potentials

Recent years have witnessed extensive developments of computer science applications in medicine - assistive technologies. Among them, the concept of Brain-Computer-Interfaces, facilitating direct communication between brain and computer, has inspired numerous practical ideas on controlling an external device via neural signals. The perception of an error made by oneself, another human or a machine, triggers an error-related potential, which has already been exploited as a binary correction readout for decisions made by Brain-ComputerInterfaces. Our approach takes advantage of this technique, while taking it one step further regarding portability by using an affordable, robust and wireless headset, the Emotiv EPOC+, to recognize error-related potentials in electroencephalograms of subjects performing various on-site, dynamic tasks. We also introduce a straightforward linear-discriminant analysis classifier that extends the range of detection from offline, post-hoc analysis, to online, within-trial recordings, an essential condition towards blending machine-performed tasks with human-generated thought processes in everyday life.

Masked and unmasked error-related potentials during continuous control and feedback

The detection of error-related potentials (ErrPs) in tasks with discrete feedback is well established in the brain–computer interface (BCI) field. However, the decoding of ErrPs in tasks with continuous feedback is still in its early stages. Objective. We developed a task in which subjects have continuous control of a cursor’s position by means of a joystick. The cursor’s position was shown to the participants in two different modalities of continuous feedback: normal and jittered. The jittered feedback was created to mimic the instability that could exist if participants controlled the trajectory directly with brain signals. Approach. This paper studies the electroencephalographic (EEG)—measurable signatures caused by a loss of control over the cursor’s trajectory, causing a target miss. Main results. In both feedback modalities, time-locked potentials revealed the typical frontal-central components of error-related potentials. Errors occurring during the jittered feedback (masked errors) were delayed in comparison to errors occurring during normal feedback (unmasked errors). Masked errors displayed lower peak amplitudes than unmasked errors. Time-locked classification analysis allowed a good distinction between correct and error classes (average Cohen-, average TPR = 81.8% and average TNR = 96.4%). Time-locked classification analysis between masked error and unmasked error classes revealed results at chance level (average Cohen-, average TPR = 60.9% and average TNR = 58.3%). Afterwards, we performed asynchronous detection of ErrPs, combining both masked and unmasked trials. The asynchronous detection of ErrPs in a simulated online scenario resulted in an average TNR of 84.0% and in an average TPR of 64.9%. Significance. The time-locked classification results suggest that the masked and unmasked errors were indistinguishable in terms of classification. The asynchronous classification results suggest that the feedback modality did not hinder the asynchronous detection of ErrPs.

Online Adaptation of a c-VEP Brain-Computer Interface(BCI) Based on Error-Related Potentials and Unsupervised Learning

The goal of a Brain-Computer Interface (BCI) is to control a computer by pure brain activity. Recently, BCIs based on code-modulated visual evoked potentials (c-VEPs) have shown great potential to establish high-performance communication. In this paper we present a c-VEP BCI that uses online adaptation of the classifier to reduce calibration time and increase performance. We compare two different approaches for online adaptation of the system: an unsupervised method and a method that uses the detection of error-related potentials. Both approaches were tested in an online study, in which an average accuracy of 96% was achieved with adaptation based on error-related potentials. This accuracy corresponds to an average information transfer rate of 144 bit/min, which is the highest bitrate reported so far for a non-invasive BCI. In a free-spelling mode, the subjects were able to write with an average of 21.3 error-free letters per minute, which shows the feasibility of the BCI system in a normal-use scenario. In addition we show that a calibration of the BCI system solely based on the detection of error-related potentials is possible, without knowing the true class labels.

Towards the detection of error-related potentials and its integration in the context of a P300 speller brain–computer interface

A P300 Speller is a brain–computer interface (BCI) that enables subjects to spell text on a computer screen by detecting P300 Event-Related Potentials in their electroencephalograms (EEG). This BCI application is of particular interest to disabled patients who have lost all means of verbal and motor communication. Error-related Potentials (ErrPs) in the EEG are generated by the subject's perception of an error. We report on the possibility of using these ErrPs for improving the performance of a P300 Speller. Overall nine subjects were tested, allowing us to study their EEG responses to correct and incorrect performances of the BCI, compare our findings to previous studies, explore the possibility of detecting ErrPs and discuss the integration of ErrP classifiers into the P300 Speller system.