A Systematic Evaluation of Euclidean Alignment with Deep Learning for EEG Decoding

Information Science, Computer Science, Artificial Intelligence, Medicine, Neurology,
brain-computer interfaces   deep learning   euclidean alignment   EEG decoding   transfer learning  

Systematic Evaluation of Euclidean Alignment with Deep Learning for EEG Decoding Background Introduction Electroencephalogram (EEG) signals are widely used in brain-computer interface (BCI) tasks due to their non-invasive nature, portability, and low acquisition cost. However, EEG signals suffer from low signal-to-noise ratio, sensitivity to electr...

GCTNet: A Graph Convolutional Transformer Network for Major Depressive Disorder Detection Based on EEG Signals

Engineering, Electrical Engineering, Information Science, Computer Science, Artificial Intelligence, Medicine, Psychiatry, Neurology, Clinical Psychology, Medical Imaging,
Major Depressive Disorder   EEG   Deep Learning   Phase Locking Value   Contrastive Learning  

GCTNet: Graph Convolution Transformer Network for Detecting Major Depressive Disorder Based on EEG Signals Research Background Major Depressive Disorder (MDD) is a prevalent mental illness characterized by significant and persistent low mood, affecting over 350 million people worldwide. MDD is one of the leading causes of suicide, resulting in appr...

Topology of Surface Electromyogram Signals: Hand Gesture Decoding on Riemannian Manifolds

Engineering, Electrical Engineering, Information Science, Electronic Information, Computer Science, Medicine, Neurology, Life Sciences, Biophysics,
surface electromyogram   hand gesture decoding   Riemannian manifold   electrode array   multivariate timeseries  

Topology of Surface Electromyography Signals: Decoding Hand Gestures Using Riemannian Manifolds This paper is authored by Harshavardhana T. Gowda (Department of Electrical and Computer Engineering, University of California, Davis) and Lee M. Miller (Center for Mind and Brain Sciences, Department of Neurophysiology and Behavior, Department of Otolar...

Preparatory Movement State Enhances Premovement EEG Representations for Brain-Computer Interfaces

Information Science, Electronic Information, Computer Science, Automation, Medicine, Neurology, Life Sciences, Bioengineering, Biophysics,
brain-computer interface   preparatory movement state   premovement intention   encoding paradigm   neural representation  

EEG of Pre-movement Phase Aids Brain-Computer Interface (BCI) in Recognizing Movement Intentions Background and Research Objectives Brain-Computer Interface (BCI) is a technology that translates human intentions directly through neural signals to control devices, holding extensive application prospects [1]. BCI has the potential to revolutionize va...

A User-Friendly Visual Brain-Computer Interface Based on High-Frequency Steady-State Visual Evoked Fields Recorded by OPM-MEG

A User-Friendly Visual Brain-Computer Interface Based on High-Frequency Steady-State Visual Evoked Fields Recorded by OPM-MEG

Engineering, Electrical Engineering, Information Science, Electronic Information, Computer Science, Automation, Artificial Intelligence, Medicine, Neurology, Medical Imaging, Life Sciences, Cell Biology, Bioengineering, Biophysics,
brain-computer interface   high frequency   steady-state visual evoked field   magnetoencephalography   optically pumped magnetometer  

Visual Brain-Computer Interface Based on High-Frequency Steady-State Visual Evoked Fields Background Brain-Computer Interface (BCI) technology allows users to control machines by decoding specific brain activity signals. While invasive BCIs excel in capturing high-quality brain signals, their application is mainly limited to clinical settings. Non-...

Auditory Cues Modulate the Short Timescale Dynamics of STN Activity During Stepping in Parkinson’s Disease

Information Science, Electronic Information, Computer Science, Automation, Artificial Intelligence, Medicine, Neurology, Neurosurgery, Life Sciences, Biophysics,
Parkinson's disease   Gait impairment   Basal ganglia   Auditory cues   Brain stimulation   Hidden Markov model   Alpha rhythms  

Patients with Parkinson’s Disease (PD) often experience gait impairments, which severely affect their quality of life. Previous studies have suggested that β-frequency (15-30 Hz) oscillatory activity in the basal ganglia may be associated with gait impairments, but the exact dynamics of these oscillations during the gait process remain unclear. Add...