The Impact of Vigorous Cycling Exercise on Visual Attention: A Study With the BR8 Wireless Dry EEG System

Machine Learning Approach for Music Familiarity Classification with Single-Channel EEG

Recognition of familiar music on brainwaves through machine learning (ML) can be instrumental in innovative therapeutic devices that improve memory and communication in dementia patients. In this study, a variety of machine learning algorithms were applied, including Random Forest (RF), Linear Discriminant Analysis (LDA), Support Vector Machine (SVM), K-Nearest Neighbor (KNN), and Deep Learning (DL), to EEG brainwaves from a mobile headset's Fp2 channel. EEG data from 20 participants assessing familiarity with 20 Christmas carols were used. For ML methods (excluding DL), particular frequency bands were selected (theta, alpha, low beta, and high beta), and six statistical features were used to train classifiers. In contrast, DL employed spectrograms and 2D convolutional neural networks. 67% accuracy was achieved with SVM using only the kurtosis features. Due to the variability of the participants, individualized training and testing produced an average accuracy of 72.4%. In dementia care, these results suggest promising therapeutic avenues.

A treadmill running research protocol to assess dynamic visual acuity and balance for athletes with and without recent concussion history

Athletes interpret dynamic visual scenes quickly and accurately during physical exertion. It is important to understand how increased exertion may impact vision and cognition following sport-related concussion (SRC).Purpose To examine the effect of a treadmill running research protocol on the assessment of dynamic visual acuity (DVA) and balance for athletes with and without recent history of SRC.Methods Varsity athletes following recent SRC (CONC=12) were compared to athletes without SRC (ATHLETE=19). The DVA task presented a Tumbling 'E' target in four possible orientations during random walk (RW) or horizontal (H) motion at a speed of 30°/s. Participants performed DVA trials standing on a force plate (1000Hz) at four time points: 1) pre-exercise (PRE-EX), 2) immediately (POST1), 3) 10-minutes (POST10), and 4) 20-minutes post- exercise (POST20). Performance was calculated as a change in DVA score from PRE-EX and median response time (RT, ms). Balance control was analyzed using the root mean square of centre of pressure displacement (dCOP).Results Both groups maintained DVA scores for both motion types and exhibited immediate exercise-induced benefits on RT. Both groups had similar change in balance control strategy following treadmill exercise.Conclusion Both groups elicited similar exercise-induced benefits on DVA following exercise. A repeated measures assessment following vigorous exercise may provide meaningful insights about visual and neurocognitive functions for athletes returning to sport following concussion.

Event-related brain potentials reveal enhancing and compensatory mechanisms during dual neurocognitive and cycling tasks

BACKGROUND

Various neurocognitive tests have shown that cycling enhances cognitive performance compared to resting. Event-related potentials (ERPs) elicited by an oddball or flanker task have clarified the impact of dual-task cycling on perception and attention. In this study, we investigate the effect of cycling on cognitive recruitment during tasks that involve not only stimulus identification but also semantic processing and memory retention.

METHODS

We recruited 24 healthy young adults (12 males, 12 females; mean age = 22.71, SD = 1.97 years) to perform three neurocognitive tasks (namely color-word matching, arithmetic calculation, and spatial working memory) at rest and while cycling, employing a within-subject design with rest/cycling counterbalancing.

RESULTS

The reaction time on the spatial working memory task was faster while cycling than at rest at a level approaching statistical significance. The commission error percentage on the color-word matching task was significantly lower at rest than while cycling. Dual-task cycling while responding to neurocognitive tests elicited the following results: (a) a greater ERP P1 amplitude, delayed P3a latency, less negative N4, and less positivity in the late slow wave (LSW) during color-word matching; (b) a greater P1 amplitude during memory encoding and smaller posterior negativity during memory retention on the spatial working memory task; and (c) a smaller P3 amplitude, followed by a more negative N4 and less LSW positivity during arithmetic calculation.

CONCLUSION

The encoding of color-word and spatial information while cycling may have resulted in compensatory visual processing and attention allocation to cope with the additional cycling task load. The dual-task cycling and cognitive performance reduced the demands of semantic processing for color-word matching and the cognitive load associated with temporarily suspending spatial information. While dual-tasking may have required enhanced semantic processing to initiate mental arithmetic, a compensatory decrement was noted during arithmetic calculation. These significant neurocognitive findings demonstrate the effect of cycling on semantic-demand and memory retention-demand tasks.

Non-invasive on-skin sensors for brain machine interfaces with epitaxial graphene

Objective. Brain-machine interfaces are key components for the development of hands-free, brain-controlled devices. Electroencephalogram (EEG) electrodes are particularly attractive for harvesting the neural signals in a non-invasive fashion.Approach.Here, we explore the use of epitaxial graphene (EG) grown on silicon carbide on silicon for detecting the EEG signals with high sensitivity.Main results and significance.This dry and non-invasive approach exhibits a markedly improved skin contact impedance when benchmarked to commercial dry electrodes, as well as superior robustness, allowing prolonged and repeated use also in a highly saline environment. In addition, we report the newly observed phenomenon of surface conditioning of the EG electrodes. The prolonged contact of the EG with the skin electrolytes functionalize the grain boundaries of the graphene, leading to the formation of a thin surface film of water through physisorption and consequently reducing its contact impedance more than three-fold. This effect is primed in highly saline environments, and could be also further tailored as pre-conditioning to enhance the performance and reliability of the EG sensors.