The Interlinking of Alpha Waves and Visuospatial Cognition in Motor-Based Domains

Selective cortical adaptations associated with neural efficiency in visuospatial tasks - the comparison of electroencephalographic profiles of expert and novice artists

Visuospatial cognition encapsulates an individual's ability to efficiently navigate and make sense of the multimodal cues from their surroundings, and therefore has been linked to expert performance across multiple domains, including sports, performing arts, and highly skilled tasks, such as drawing (Morrone and Minini, 2023). As neural efficiency posits a task-specific functional reorganization facilitated by long-term training, the present study employs a visuospatial construction task as a means of investigating the neurophysiological adaptations associated with expert visuospatial cognitive performance. Electroencephalogram (EEG) data acquisitions were used to evaluate the event-related changes (ER%) and statistical topographic maps of nine expert versus nine novice artists. The expert artists displayed overall higher global ER% compared to the novices within task-active intervals. Significant increases in relative ER% were found in the theta (t (10) = 3.528, p = 0.003, CI = [27.3,120.9]), lower-alpha (t (10) = 3.751, p = 0.002, CI = [28.2,110.5]), upper-alpha (t (10) = 3.829, p = 0.002, CI = [50.2,189.8]), and low beta (t (10) = 4.342, p < 0.001, CI = [37.0,114.9]) frequency bands, when comparing the experts to the novice participants. These results were particularly found in the frontal (t (14) = 2.014, p = 0.032, CI = [7.7,245.4]) and occipital (t (14) = 2.647, p = 0.010, CI = [45.0,429.7]) regions. Further, a significant decrease in alpha ER% from lower to upper activity (t (8) = 4.475, p = 0.001, CI = [21.0, 65.8]) was found across cortical regions in the novice group. Notably, greater deviation between lower and upper-alpha activity was found across scalp locations in the novice group, compared to the experts. Overall, the findings demonstrate potential local and global EEG-based indices of selective cortical adaptations within a task requiring a high degree of visuospatial cognition, although further work is needed to replicate these findings across other domains.

EEG-based neurophysiological indices for expert psychomotor performance - a review

A primary objective of current human neuropsychological performance research is to define the physiological correlates of adaptive knowledge utilization, in order to support the enhanced execution of both simple and complex tasks. Within the present article, electroencephalography-based neurophysiological indices characterizing expert psychomotor performance, will be explored. As a means of characterizing fundamental processes underlying efficient psychometric performance, the neural efficiency model will be evaluated in terms of alpha-wave-based selective cortical processes. Cognitive and motor domains will initially be explored independently, which will act to encapsulate the task-related neuronal adaptive requirements for enhanced psychomotor performance associating with the neural efficiency model. Moderating variables impacting the practical application of such neuropsychological model, will also be investigated. As a result, the aim of this review is to provide insight into detectable task-related modulation involved in developed neurocognitive strategies which support heightened psychomotor performance, for the implementation within practical settings requiring a high degree of expert performance (such as sports or military operational settings).

Eye State Detection Using Frequency Features from 1 or 2-Channel EEG

Brain-computer interfaces (BCIs) establish a direct communication channel between the human brain and external devices. Among various methods, electroencephalography (EEG) stands out as the most popular choice for BCI design due to its non-invasiveness, ease of use, and cost-effectiveness. This paper aims to present and compare the accuracy and robustness of an EEG system employing one or two channels. We present both hardware and algorithms for the detection of open and closed eyes. Firstly, we utilize a low-cost hardware device to capture EEG activity from one or two channels. Next, we apply the discrete Fourier transform to analyze the signals in the frequency domain, extracting features from each channel. For classification, we test various well-known techniques, including Linear Discriminant Analysis (LDA), Support Vector Machine (SVM), Decision Tree (DT), or Logistic Regression (LR). To evaluate the system, we conduct experiments, acquiring signals associated with open and closed eyes, and compare the performance between one and two channels. The results demonstrate that employing a system with two channels and using SVM, DT, or LR classifiers enhances robustness compared to a single-channel setup and allows us to achieve an accuracy percentage greater than 95% for both eye states.