Task-specificity in isolated focal dystonias is a powerful feature that may successfully be targeted with therapeutic brain-computer interfaces. While performing a symptomatic task, the patient actively modulates momentary brain activity (disorder signature) to match activity during an asymptomatic task (target signature), which is expected to translate into symptom reduction.
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http://dx.doi.org/10.1002/mds.29178 | DOI Listing |
Neurosci Biobehav Rev
January 2025
Experimental Therapeutics and Pathophysiology Branch, National Institute of Mental Health, National Institutes of Health, Bethesda, MD, USA; Department of Psychiatry and Psychotherapy, Jena University Hospital, Jena, Germany. Electronic address:
Understanding how the brain distinguishes emotional from neutral scenes is crucial for advancing brain-computer interfaces, enabling real-time emotion detection for faster, more effective responses, and improving treatments for emotional disorders like depression and anxiety. However, inconsistent research findings have arisen from differences in study settings, such as variations in the time windows, brain regions, and emotion categories examined across studies. This review sought to compile the existing literature on the timing at which the adult brain differentiates basic affective from neutral scenes in less than one second, as previous studies have consistently shown that the brain can begin recognizing emotions within just a few milliseconds.
View Article and Find Full Text PDFAlzheimers Dement
December 2024
University of Washington, Seattle, WA, USA.
Background: A multitude of high-quality imaging modalities exist that provide structural data at unprecedented levels of detail. Tissue ultrastructure greatly influences the rate of transport of proteins and other molecules that contribute to neurodegeneration. However, our ability to model flow and diffusion processes in the brain lags behind the quality of the neuroimaging data.
View Article and Find Full Text PDFSci Rep
January 2025
Department of Biomedical Engineering, School of Biomedical Engineering, Tsinghua University, Beijing, 100084, China.
Steady-State Visually Evoked Potential (SSVEP) signals can be decoded by either a traditional machine learning algorithm or a deep learning network. Combining the two methods is expected to enhance the performance of an SSVEP-based brain-computer interface (BCI) by exploiting their advantages. However, an efficient strategy for integrating the two methods has not yet been established.
View Article and Find Full Text PDFAnnu Rev Biomed Eng
January 2025
Department of Neurological Surgery, University of California, Davis, California, USA; email:
People who have lost the ability to speak due to neurological injuries would greatly benefit from assistive technology that provides a fast, intuitive, and naturalistic means of communication. This need can be met with brain-computer interfaces (BCIs): medical devices that bypass injured parts of the nervous system and directly transform neural activity into outputs such as text or sound. BCIs for restoring movement and typing have progressed rapidly in recent clinical trials; speech BCIs are the next frontier.
View Article and Find Full Text PDFNeural Netw
December 2024
The school of Electrical and Information Engineering, Tianjin University, Tianjin 300072, China. Electronic address:
Emotion recognition via electroencephalogram (EEG) signals holds significant promise across various domains, including the detection of emotions in patients with consciousness disorders, assisting in the diagnosis of depression, and assessing cognitive load. This process is critically important in the development and research of brain-computer interfaces, where precise and efficient recognition of emotions is paramount. In this work, we introduce a novel approach for emotion recognition employing multi-scale EEG features, denominated as the Dynamic Spatial-Spectral-Temporal Network (DSSTNet).
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