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Time-frequency-space transformer EEG decoding for spinal cord injury.
Cogn Neurodyn
December 2024
Rehabilitation and Physical Therapy Department, Shandong University of Traditional Chinese Medicine Affiliated Hospital, NO.42, Wenhuaxi Road, Jinan, 250014 Shandong Province People's Republic of China.
Transformer neural networks based on multi-head self-attention are effective in several fields. To capture brain activity on electroencephalographic (EEG) signals and construct an effective pattern recognition model, this paper explores the multi-channel deep feature decoding method utilizing the self-attention mechanism. By integrating inter-channel features with intra-channel features, the self-attention mechanism generates a deep feature vector that encompasses information from all brain activities.
View Article and Find Full Text PDFElectroencephalographic signatures of migraine in small prospective and large retrospective cohorts.
Sci Rep
November 2024
Department of Biomedical Engineering, Cleveland Clinic Foundation, Cleveland, OH, USA.
Migraine is one of the most common neurological disorders in the US. Currently, the diagnosis and management of migraine are based primarily on subjective self-reported measures, which compromises the reliability of clinical diagnosis and the ability to robustly discern candidacy for available therapies and track treatment response. In this study, we used a computational pipeline for the automated, rapid, high-throughput, and objective analysis of encephalography (EEG) data at Cleveland Clinic to identify signatures that correlate with migraine.
View Article and Find Full Text PDFAutomatic responsiveness testing in epilepsy with wearable technology: The ARTiE Watch.
Epilepsia
November 2024
Department of Neurology, Yale School of Medicine, New Haven, Connecticut, USA.
Article Synopsis
- The study aimed to create a mobile platform called ARTiE Watch to objectively and consistently assess behavioral responsiveness in people with epilepsy during and after seizures, improving current subjective methods.
- Patients were recruited to wear the ARTiE Watch, which paired with a smartphone app to remotely initiate standardized tests evaluating motor, language, and memory responses upon seizure identification.
- Results showed that participants had significantly decreased behavioral responsiveness during seizures compared to their baseline, with marked differences noted in specific types of seizures like bilateral tonic-clonic seizures.
Dynamic functional connectivity correlates of mental workload.
Cogn Neurodyn
October 2024
Center for Cognition and Neuroergonomics, State Key Laboratory of Cognitive Neuroscience and Learning, Beijing Normal University, Zhuhai, 519087 China.
Unlabelled: Tasks with high mental workload often involve higher cognitive functions of the human brain and complex information flow involving multiple brain regions. However, the dynamics of functional connectivity between brain regions during high mental workload have not been well-studied. We use an analysis approach designed to find repeating network states from gamma-band phase locking value networks built from electroencephalograph data collected while participants engaged in tasks with different levels of mental workload.
View Article and Find Full Text PDFRELAX-Jr: An Automated Pre-Processing Pipeline for Developmental EEG Recordings.
Hum Brain Mapp
October 2024
Monarch Research Institute, Monarch Mental Health Group, Sydney, New South Wales, Australia.
Automated EEG pre-processing pipelines provide several key advantages over traditional manual data cleaning approaches; primarily, they are less time-intensive and remove potential experimenter error/bias. Automated pipelines also require fewer technical expertise as they remove the need for manual artefact identification. We recently developed the fully automated Reduction of Electroencephalographic Artefacts (RELAX) pipeline and demonstrated its performance in cleaning EEG data recorded from adult populations.
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