Machine learning and signal-processing methods for analyzing neural recordings and extracting meaningful temporal or spatial patterns.
Projects can include preprocessing, artifact handling, feature extraction, classification, regression, representation learning, and temporal modeling.
Applications include EEG, MEG, electrophysiological recordings, behavioral-neural datasets, and computational neuroscience research.
Implementation is selected according to the available data, technical constraints, required level of automation, and the existing software or research environment. The solution can be implemented as a standalone component or integrated into a larger system.
The resulting system is intended to provide a clear computational workflow that can be evaluated, maintained, and extended as the project develops. Model choice, data processing, interfaces, and deployment can therefore be adapted to the requirements of the specific project.