Pairing voluntary movement and muscle-located electrical stimulation increases cortical excitability

Learning new motor skills has been correlated with increased cortical excitability. In this study, different location of electrical stimulation (ES), nerve or muscle, was paired with voluntary movement to investigate if ES paired with voluntary movement a) would increase the excitability of cortical projections to tibialis anterior and b) if stimulation location mattered. Cortical excitability changes were quantified using motor evoked potentials (MEPs) elicited by transcranial magnetic stimulation at varying intensities during four conditions. Twelve healthy subjects performed 50 dorsiflexions at the ankle during nerve or muscle ES at motor threshold. ES alone was delivered 50 times and the movement was performed 50 times. A significant increase in the excitability from pre- to post-intervention (P=0.0061) and pre- to 30 minutes post-intervention (P=0.017) measurements was observed when voluntary movement was paired with muscle ES located at tibialis anterior. An increase of 50±57% and 28±54% in the maximum MEPs was obtained for voluntary movement paired with muscle-located and nerve-located ES, respectively. The maximum MEPs for voluntary movement alone and muscle-located ES alone were -5±28% and 2±42%, respectively. Pairing voluntary movement with muscle-located ES increases excitability of corticospinal projections of tibialis anterior in healthy participants. This finding suggests that active participation during muscle-located ES protocols increases cortical excitability to a greater extent than stimulation alone. The next stage of this research is to investigate the effect in people with stroke. The results may have implications for motor recovery in patients with motor impairments following neurological injury

Non-Linear Adapted Spatio-Temporal Filter for Single-Trial Identification of Movement-Related Cortical Potential

The execution or imagination of a movement is reflected by a cortical potential that can be recorded by electroencephalography (EEG) as Movement-Related Cortical Potentials (MRCPs). The identification of MRCP from a single trial is a challenging possibility to get a natural control of a Brain–Computer Interface (BCI). We propose a novel method for MRCP detection based on optimal non-linear filters, processing different channels of EEG including delayed samples (getting a spatio-temporal filter). Different outputs can be obtained by changing the order of the temporal filter and of the non-linear processing of the input data. The classification performances of these filters are assessed by cross-validation on a training set, selecting the best ones (adapted to the user) and performing a majority voting from the best three to get an output using test data. The method is compared to another state-of-the-art filter recently introduced by our group when applied to EEG data recorded from 16 healthy subjects either executing or imagining 50 self-paced upper-limb palmar grasps. The new approach has a median accuracy on the overall dataset of 80%, which is significantly better than that of the previous filter (i.e., 63%). It is feasible for online BCI system design with asynchronous, self-paced applications

Relationship between decision making styles and consumer behavior

The objective of this study is to identify the different factors that impact on consumer behavior on the bases of decision making style. The sample size of this study was two hundred and fifty. Data was collected through five point Likert scale questionnaire. SPSS was used to analyze the data. Correlation and multiple regression was performed to measure the relationship between independent and dependent variables and independent sample t-test was applied to test difference between male and female decision making style. This study concludes that there is significant relationship between male and female decision making style and female respondents are more agreed that decision making styles influence the consumer behavior. All the independent variables have significant and positive relationship with consumer behavior

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

Efficacy of a Single-Task ERP Measure to Evaluate Cognitive Workload During a Novel Exergame

This study aimed to validate the efficacy of single-task event-related potential (ERP) measures of cognitive workload to be implemented in exergame-based rehabilitation. Twenty-four healthy participants took part in a novel gamified balance task where task-irrelevant auditory tones were presented in the background to generate ERPs in the participants’ electroencephalogram (EEG) as a measure of cognitive workload. For the balance task, a computer-based tilt-ball game was combined with a balance board. Participants played the game by shifting their weight to tilt the balance board, which moved a virtual ball to score goals. The game was manipulated by adjusting the size of the goalposts to set three predefined levels of game difficulty (easy, medium, and hard). The participant’s experience of game difficulty was evaluated based on the number of goals scored and their subjective reporting of perceived difficulty. Participants experienced a significant difference in the three levels of task difficulty based on the number of goals scored and perceived difficulty (p < 0.001). Post hoc analysis revealed the lowest performance for the hardest level. The mean amplitude of the N1 ERP component was used to measure the cognitive workload associated with the three difficulty levels. The N1 component’s amplitude decreased significantly (p < 0.001), with an increase in the task difficulty. Moreover, the amplitude of the N1 component for the hard level was significantly smaller compared to medium (p = 0.0003) and easy (p < 0.001) levels. These results support the efficacy of the N1 ERP component to measure cognitive workload in dynamic and real-life scenarios such as exergames and other rehabilitation exercises