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 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

Applied Error Related Negativity: Single Electrode Electroencephalography in Complex Visual Stimuli

Error related negativity (ERN) is a pronounced negative evoked response potential (ERP) that follows a known error. This neural pattern has the potential to communicate user awareness of incorrect actions within milliseconds. While the implications for human-machine interface and augmented cognition are exciting, the ERN has historically been evoked only in the laboratory using complex equipment while presenting simple visual stimuli such as letters and symbols. To effectively harness the applied potential of the ERN, detection must be accomplished in complex environments using simple, preferably single-electrode, EEG systems feasible for integration into field and workplace-ready equipment. The present project attempted to use static photographs to evoke and successfully detect the ERN in a complex visual search task: motorcycle conspicuity. Drivers regularly fail to see motorcycles, with tragic results. To reproduce the issue in the lab, static pictures of traffic were presented, either including or not including motorcycles. A standard flanker letter task replicated from a classic ERN study (Gehring et al., 1993) was run alongside, with both studies requiring a binary response. Results showed that the ERN could be clearly detected in both tasks, even when limiting data to a single electrode in the absence of artifact correction. These results support the feasibility of applied ERN detection in complex visual search in static images. Implications and opportunities will be discussed, limitations of the study explained, and future directions explored

Errare machinale est: The use of error-related potentials in brain-machine interfaces

The ability to recognize errors is crucial for efficient behavior. Numerous studies have identified electrophysiological correlates of error recognition in the human brain (error-related potentials, ErrPs). Consequently, it has been proposed to use these signals to improve human-computer interaction (HCI) or brain-machine interfacing (BMI). Here, we present a review of over a decade of developments towards this goal. This body of work provides consistent evidence that ErrPs can be successfully detected on a single-trial basis, and that they can be effectively used in both HCI and BMI applications. We first describe the ErrP phenomenon and follow up with an analysis of different strategies to increase the robustness of a system by incorporating single-trial ErrP recognition, either by correcting the machine's actions or by providing means for its error-based adaptation. These approaches can be applied both when the user employs traditional HCI input devices or in combination with another BMI channel. Finally, we discuss the current challenges that have to be overcome in order to fully integrate ErrPs into practical applications. This includes, in particular, the characterization of such signals during real(istic) applications, as well as the possibility of extracting richer information from them, going beyond the time-locked decoding that dominates current approaches

Υπολογιστική γνωσιακή εκπαίδευση σε MATLAB με χρήση ηλεκτροεγκεφαλογραφίας για βελτίωση της μνήμης προσώπων-ονομάτων

Η παρούσα εργασία εξετάζει τη δυνατότητα εφαρμογής γνωσιακής εκπαίδευσης για τη βελτίωση της μνήμης προσώπων-ονομάτων και την επίδραση αυτής σε άλλες γνωσιακές εργασίες. Η εκπαίδευση πραγματοποιήθηκε με εφαρμογή της διαδικασίας επανάληψης με υστέρηση, ενώ οι εργασίες μεταφοράς που εξετάστηκαν είναι οι N-back και Verbal Paired Associates, οι οποίες αξιολογούν λειτουργική και λεκτική μνήμη αντίστοιχα. Τόσο η εκπαίδευση όσο και η αξιολόγηση των συμμετεχόντων πραγματοποιήθηκαν μέσω του MATLAB, ενώ ταυτόχρονα καταγράφονταν δεδομένα EEG μέσω του MindWave Mobile, τα οποία αξιοποιήθηκαν για τη μελέτη της απόδοσης στις εργασίες. Αν και η στατιστική ανάλυση επιβεβαίωσε ότι το πλήθος των συμμετεχόντων (6) ήταν μικρό προκειμένου να εξαχθούν ασφαλή συμπεράσματα, παρατηρήθηκε στατιστικά σημαντική διαφορά σε συγκεκριμένες πτυχές της εγκεφαλικής δραστηριότητας (κυρίως στις ζώνες α και θ), καθώς και επίτευξη μεταφοράς στα αποτελέσματα της εργασίας N-back. Επιπλέον, επιβεβαιώθηκε σαφώς η διάκριση μεταξύ αυτόματων και ελεγχόμενων μνημονικών διεργασιών, οι οποίες διαδραματίζουν σημαντικό ρόλο τόσο κατά την αύξηση της ηλικίας όσο και σε περιπτώσεις γνωσιακών διαταραχών.This thesis examines the application of cognitive training to face-name memory improvement and possible transfer effects to other cognitive tasks. Training was conducted using the repetition-lag procedure, while the N-back task and the Verbal Paired Assosiates test were used as transfer tasks, evaluating working memory and verbal memory respectively. Both training and evaluation were conducted using MATLAB. At the same time, EEG data were recorded via MindWave Mobile and were utilized in the study of the participants’ performance. Although the number of the participants (6) was too small to draw safe conclusions, a statistically significant difference was observed in various aspects of brain activity (mainly in alpha and theta bands), while transfer effects for N-back task also appeared. Moreover, there was clear confirmation of the distinction between automatic and effortful memory processes, which play an important role in both aging and cognitive impairments

Salience and motivated behaviour in schizophrenia

Schizophrenia is a long-term psychotic disorder that affects approximately 1% of the population worldwide. Schizophrenia is characterised by negative symptoms, such as anhedonia and social withdrawal, and positive symptoms, such as hallucinations and delusions. The impact of schizophrenia reaches beyond the impaired social and cognitive function of the individual, affecting families and wider communities. Therefore, despite its low prevalence, there is a long history of multidisciplinary research investigating the causes of schizophrenia. The effect of antipsychotics in reducing the intensity of symptoms, through their antagonistic effect on dopamine, has led to dopaminergic based theories of schizophrenia. One such theory is based on aberrant salience, the assignment of importance to stimuli that have no intrinsic or learned value or salience. The aberrant salience hypothesis links hyperdopaminergic activation to symptoms of schizophrenia through the intermediary effect of motivational salience. Specifically, it is proposed that hyperdopaminergic activation in schizophrenia creates an aberrant motivational association with a stimulus, leading to cognitive explanations for the unexplained importance that contribute to the development of symptoms. Behavioural and neural evidence supports heightened aberrant salience in schizophrenia, although specific measures of aberrant salience have yielded inconsistent results. There is also a large body of evidence suggesting cognitive functions anchored in dopaminergic activation, such as reward processing and motivated behaviour, are impaired in schizophrenia. To date, however, the assumption that motivational salience mediates the relationship between hyperdopaminergic activation and aberrant salience has not been tested. The current project sought to elucidate the relationship between aberrant salience and motivational salience. The convergent validity among measures of aberrant salience (Salience Attribution Task and Aberrant Salience Inventory) and motivated behaviour (Effort Expenditure for Rewards Task and Stimulus Chase Task) were investigated in undergraduates. To assess whether aberrant salience, and the underlying relationship with motivational salience, is unique to schizophrenia, the same measures were completed by individuals diagnosed with schizophrenia, experiencing symptoms of anxiety, or unaffected by mental health. Whereas schizophrenia was associated with heightened aberrant salience, the aberrant salience indices lacked specificity, sensitivity, and convergent validity. Furthermore, whereas schizophrenia was associated with maladaptive motivated behaviour, there was limited evidence supporting a relationship between measures of aberrant salience and motivational salience. The failure to find evidence of such a relationship may be due to issues with the aberrant salience measures or the underlying assumption that motivational salience mediates aberrant salience. Further research is needed to develop measures of aberrant salience that are anchored to known neural systems underlying salience processing