Browsing by Author "Kinney-Lang, Eli"
Now showing 1 - 2 of 2
Results Per Page
Sort Options
- ItemOpen AccessExploring Markers of Brain-Computer Interface Performance in Children(2024-05-14) Kim, Vella Shin-Hyung; Kirton, Adam; Abou-Zeid, Hatem; Condliffe, Elizabeth; Kinney-Lang, EliFor children with severe motor impairments, brain-computer interfaces (BCIs) are a potential life changing solution that provide an alternate means of communication and control. Some BCI users experience difficulties controlling a motor-imagery BCI (MI-BCI), but the unique factors influencing BCI performance in children are largely understudied. This study aimed to build predictive models of BCI performance in typically developing children using EEG correlates, demographic factors, and subjective assessments. We also aimed to explore specific features most predictive of BCI performance in children. Two datasets, DS1 (n=31) and DS2 (n=22) from independent studies comprising of EEG data, demographic information, and subjective assessments from typically-developing children were utilized. Models were trained 15 times, each on different feature subsets from DS1 using Support Vector Machine (SVM) and Random Forest (RF) classifiers, with hyperparameter optimization conducted using a differential genetic particle swarm optimizer. Models were internally (within-distribution) and externally (out-of-distribution using DS2) tested using repeated stratified k-fold cross-validation. We assessed the most commonly selected features for each feature set and the SHapley Additive exPlanations (SHAP) scores of the highest performing models. SVM models trained on EEG features outperformed models trained with all features or non-EEG features. The highest performing model was an SVM model trained on EEG features, with a cross-validation score, internal test score, and external test score of 0.76±0.24, 0.75, and 0.77 respectively. Connectivity and power in the alpha and theta bands were most predictive of BCI performance in our highest performing models. Our investigations provide a tool to predict BCI performance in children, and allow a fuller understanding of the internal factors influencing BCI performance. Predicting BCI performance in children can help to understand when standard BCI paradigms may not work for users and indicate when alternative methods of training may be necessary, ultimately improving the usability of BCIs in children.
- ItemOpen AccessFatigue in children using motor imagery and P300 brain-computer interfaces(2024-04-24) Keough, Joanna R.; Irvine, Brian; Kelly, Dion; Wrightson, James; Comaduran Marquez, Daniel; Kinney-Lang, Eli; Kirton, AdamAbstract Background Brain-computer interface (BCI) technology offers children with quadriplegic cerebral palsy unique opportunities for communication, environmental exploration, learning, and game play. Research in adults demonstrates a negative impact of fatigue on BCI enjoyment, while effects on BCI performance are variable. To date, there have been no pediatric studies of BCI fatigue. The purpose of this study was to assess the effects of two different BCI paradigms, motor imagery and visual P300, on the development of self-reported fatigue and an electroencephalography (EEG) biomarker of fatigue in typically developing children. Methods Thirty-seven typically-developing school-aged children were recruited to a prospective, crossover study. Participants attended three sessions: (A) motor imagery-BCI, (B) visual P300-BCI, and (C) video viewing (control). The motor imagery task involved an imagined left- or right-hand squeeze. The P300 task involved attending to one square on a 3 × 3 grid during a random single flash sequence. Each paradigm had respective calibration periods and a similar visual counting game. Primary outcomes were self-reported fatigue and the power of the EEG alpha band both collected during resting-state periods pre- and post-task. Self-reported fatigue was measured using a 10-point visual analog scale. EEG alpha band power was calculated as the integrated power spectral density from 8 to 12 Hz of the EEG spectrum. Results Thirty-two children completed the protocol (age range 7–16, 63% female). Self-reported fatigue and EEG alpha band power increased across all sessions (F(1,155) = 33.9, p < 0.001; F = 5.0(1,149), p = 0.027 respectively). No differences in fatigue development were observed between session types. There was no correlation between self-reported fatigue and EEG alpha band power change. BCI performance varied between participants and paradigms as expected but was not associated with self-reported fatigue or EEG alpha band power. Conclusion Short periods (30-mintues) of BCI use can increase self-reported fatigue and EEG alpha band power to a similar degree in children performing motor imagery and P300 BCI paradigms. Performance was not associated with our measures of fatigue; the impact of fatigue on useability and enjoyment is unclear. Our results reflect the variability of fatigue and the BCI experience more broadly in children and warrant further investigation.