AI and Machine Learning

A model that dynamically adjusts its decision boundaries according to changing neural patterns or user adaptation.

Computer systems that mimic human cognitive functions to interpret brain signals and provide adaptive control over them.

A neural network that compresses and reconstructs brain data for feature extraction or noise reduction purposes.

A probabilistic model that estimates the likelihood of specific mental states occurring.

A machine learning algorithm that learns to assign a label (e.g., move left or select letter B) to a pattern of neural activity. Common examples include

A deep learning model effective at detecting spatial structures within EEG or neuroimaging data.

A statistical method to evaluate model performance across different subsets of brain data.

Statistical method to assess how well a predictive model will generalize to new, unseen neural data.

Generating synthetic neural samples to balance datasets or improve training stability.

Multi-layered neural networks capable of automatically extracting complex spatio-temporal features from EEG or fMRI data.

Multilayer network mapping complex neural patterns to behavioral outputs.

Simplifying neural data while preserving essential information (e.g., using PCA or t-SNE).

Combining multiple classifiers to improve overall accuracy and reliability.

One complete pass of the entire training dataset through the artificial neural network

Techniques aimed at making AI-based neural decoding understandable and transparently interpretable.

Automatic extraction of distinctive patterns from complex neural data.

Choosing the most informative signal features to enhance classifier performance.

A distributed learning approach that allows collaborative training without sharing raw brain data.

Systematic tuning of model parameters to achieve optimal neural decoding performance.

A simple classification method based on measuring the distance between neural feature vectors.