Sex-related differences in visuomotor skill recovery following concussion in working-aged adults

Smeha, Nicole; Kalkat, Ravneet; Sergio, Lauren E.; Hynes, Loriann M.

doi:10.1186/s13102-022-00466-6

Cited by 3 publications

(1 citation statement)

References 68 publications

(82 reference statements)

Supporting

Mentioning

Contrasting

Order By: Relevance

“…The task took ∼5 min to complete. Previous findings from our groups have reliably demonstrated the sensitivity of this task to detect brain network dysfunction during the control of rulebased visuomotor skill performance (Salek et al, 2011;Hawkins and Sergio, 2014;Hawkins et al, 2015;Hurtubise et al, 2016Hurtubise et al, , 2020Sergio et al, 2020;Smeha et al, 2022).…”

Section: Experimental Tasksupporting

confidence: 53%

Machine learning algorithms for detection of visuomotor neural control differences in individuals with PASC and ME

Ahuja,

Badhwar,

Edgell

et al. 2024

Front. Hum. Neurosci.

Self Cite

View full text Add to dashboard Cite

The COVID-19 pandemic has affected millions worldwide, giving rise to long-term symptoms known as post-acute sequelae of SARS-CoV-2 (PASC) infection, colloquially referred to as long COVID. With an increasing number of people experiencing these symptoms, early intervention is crucial. In this study, we introduce a novel method to detect the likelihood of PASC or Myalgic Encephalomyelitis (ME) using a wearable four-channel headband that collects Electroencephalogram (EEG) data. The raw EEG signals are processed using Continuous Wavelet Transform (CWT) to form a spectrogram-like matrix, which serves as input for various machine learning and deep learning models. We employ models such as CONVLSTM (Convolutional Long Short-Term Memory), CNN-LSTM, and Bi-LSTM (Bidirectional Long short-term memory). Additionally, we test the dataset on traditional machine learning models for comparative analysis. Our results show that the best-performing model, CNN-LSTM, achieved an accuracy of 83%. In addition to the original spectrogram data, we generated synthetic spectrograms using Wasserstein Generative Adversarial Networks (WGANs) to augment our dataset. These synthetic spectrograms contributed to the training phase, addressing challenges such as limited data volume and patient privacy. Impressively, the model trained on synthetic data achieved an average accuracy of 93%, significantly outperforming the original model. These results demonstrate the feasibility and effectiveness of our proposed method in detecting the effects of PASC and ME, paving the way for early identification and management of the condition. The proposed approach holds significant potential for various practical applications, particularly in the clinical domain. It can be utilized for evaluating the current condition of individuals with PASC or ME, and monitoring the recovery process of those with PASC, or the efficacy of any interventions in the PASC and ME populations. By implementing this technique, healthcare professionals can facilitate more effective management of chronic PASC or ME effects, ensuring timely intervention and improving the quality of life for those experiencing these conditions.

show abstract