encodingdecodingmodell

An encoding-decoding model is a framework used in cognitive neuroscience, psychology, and machine learning to relate external stimuli to brain activity and to infer stimuli from brain data. The encoding component builds a model that predicts neural responses from features of the stimulus, while the decoding component uses neural responses to predict or reconstruct the stimulus.

Encoding models typically employ linear mappings such as ridge or elastic net regression, though nonlinear extensions and machine learning methods are also used. They rely on representations of the stimulus, which can include acoustic spectrograms, visual features, semantic properties, or other task-relevant descriptors. Data are aligned with neural measurements, often accounting for temporal dynamics such as the hemodynamic response in fMRI or real-time responses in EEG/MEG.

The decoding side attempts to invert the encoding process: given brain activity, it predicts the presented stimulus or reconstructs aspects of it. This is common in brain-computer interface research and in studies of perception and memory. The usual workflow includes collecting data while participants experience stimuli, extracting feature representations, training models with cross-validation, and evaluating performance with metrics such as explained variance, correlation, or accuracy.

Applications span functional neuroimaging, sensory and language research, and BCI development. Strengths of encoding-decoding models include interpretability and the ability to link neural signals to specific stimulus features. Limitations involve data requirements, potential overfitting, and challenges in modeling nonlinear neural dynamics or individual variability. Related concepts include forward encoding models, MVPA, representational similarity analysis, and nonlinear or deep learning approaches for more complex mappings.