P300 latency jitter and its correlates in people with amyotrophic lateral sclerosis

Effects of an acute bout of cycling on different domains of cognitive function

The literature suggesting acute exercise benefits cognitive function has been largely confined to single cognitive domains and measures of reliant on measures of central tendencies. Furthermore, studies suggest cognitive intra-individual variability (IIV) to reflect cognitive efficiency and provide unique insights into cognitive function, but there is limited knowledge on the effects of acute exercise on IIV. To this end, this study examined the effects of acute exercise on three different cognitive domains, executive function, implicit learning, and hippocampal-dependent memory function using behavioral performance and event-related potentials (ERPs). Furthermore, this study also sought to explore the effects of an acute bout of exercise on IIV using the RIDE algorithm to separate signals into individuals components based on latency variability. Healthy adult participants (N=20; 26.3±4.8years) completed a randomized cross-over trial with seated rest or 30min of high intensity cycling. Before and after each condition, participants completed a cognitive battery consisting of the Eriksen Flanker task, implicit statistical learning task, and a spatial reconstruction task. While exercise did not affect Flanker or spatial reconstruction performance, there were exercise related decreases in accuracy (F=5.47; P=0.040), slowed reaction time (F=5.18; P=0.036), and decreased late parietal positivity (F=4.26; P=0.046). However, upon adjusting for performance and ERP variability, there were exercise related decreases in Flanker reaction time (F=24.00; P<0.001), and reduced N2 amplitudes (F=13.03; P=0.002), and slower P3 latencies (F=3.57; P=0.065) for incongruent trials. These findings suggest that acute exercise may impact cognitive IIV as an adaptation to maintain function following exercise.

Multimodal pre-screening can predict BCI performance variability: A novel subject-specific experimental scheme

BACKGROUND

Brain-computer interface (BCI) systems currently lack the required robustness for long-term daily use due to inter- and intra-subject performance variability. In this study, we propose a novel personalized scheme for a multimodal BCI system, primarily using functional near-infrared spectroscopy (fNIRS) and electroencephalography (EEG), to identify, predict, and compensate for factors affecting competence-related and interfering factors associated with performance.

METHOD

11 (out of 13 recruited) participants, including five participants with motor deficits, completed four sessions on average. During the training sessions, the subjects performed a short pre-screening phase, followed by three variations of a novel visou-mental (VM) protocol. Features extracted from the pre-screening phase were used to construct predictive platforms using stepwise multivariate linear regression (MLR) models. In the test sessions, we employed a task-correction phase where our predictive models were used to predict the ideal task variation to maximize performance, followed by an interference-correction phase. We then investigated the associations between predicted and actual performances and evaluated the outcome of correction strategies.

RESULT

The predictive models resulted in respective adjusted R-squared values of 0.942, 0.724, and 0.939 for the first, second, and third variation of the task, respectively. The statistical analyses showed significant associations between the performances predicted by predictive models and the actual performances for the first two task variations, with rhos of 0.7289 (p-value = 0.011) and 0.6970 (p-value = 0.017), respectively. For 81.82 % of the subjects, the task/workload correction stage correctly determined which task variation provided the highest accuracy, with an average performance gain of 5.18 % when applying the correction strategies.

CONCLUSION

Our proposed method can lead to an integrated multimodal predictive framework to compensate for BCI performance variability, particularly, for people with severe motor deficits.

Study of the human brain potentials variability effects in P300 based brain–computer interface

The P300-based brain–computer interfaces (P300 BCI) allow the user to select commands by focusing on them. The technology involves electroencephalographic (EEG) representation of the event-related potentials (ERP) that arise in response to repetitive external stimulation. Conventional procedures for ERP extraction and analysis imply that identical stimuli produce identical responses. However, the floating onset of EEG reactions is a known neurophysiological phenomenon. A failure to account for this source of variability may considerably skew the output and undermine the overall accuracy of the interface. This study aimed to analyze the effects of ERP variability in EEG reactions in order to minimize their influence on P300 BCI command classification accuracy. Healthy subjects aged 21–22 years (n = 12) were presented with a modified P300 BCI matrix moving with specified parameters within the working area. The results strongly support the inherent significance of ERP variability in P300 BCI environments. The correction of peak latencies in single EEG reactions provided a 1.5–2 fold increase in ERP amplitude with a concomitant enhancement of classification accuracy (from 71–78% to 92–95%, p < 0.0005). These effects were particularly pronounced in attention-demanding tasks with the highest matrix velocities. The findings underscore the importance of accounting for ERP variability in advanced BCI systems.