Brain-computer interfacing using EEG signals and AI

Have been working in Brain-computer interfacing projcets involving EEG signals and machine/deep learning for 10 years and developed a toolbox for the analyses of EEGs as well as classification models, this toolbox is called lively vectors:

latest project demo:

Can be used for EEG analyses and other multivariate data, can be used with videos, speech, data from sensors, etc.

Can be given custom-built pipeline steps and perform them, it includes visualisation with statistical outcomes.

Includes segmentation, filtering, and cleaning of signals and rejection of noisy samples/trials/channels automatically and via visual inspection.

Includes different methods for feature extraction.

Includes different analyses such as: time domain analyses ( ERP analysis ), frequency domain analysis, time frequency analysis (TF analysis), spatial filtering, source separation, intertrial phase consistency (ITPC), cross-correlation and signal alignment, phase amplitude coupling (PAC), representative similarity analysis (RSA), LDA beamforming, and more.

Includes classification:Linear discriminant analysis (LDA), support vector machine (SVM), random forests, Riemannian geometry-based classifiers . Neural networks (NNs), Recurrent neural networks (RNNs), Convolutional neural networks (CNNs) for classification and feature extraction, Temporal convolutional networks (TCN) and more classifiers.

All classifiers can be applied on single data points, across temporal dimension, and in a 2d temporal generalisation plot as well as across space as a searchlight. It has different classification and pre-processing options e.g., cross-validation (CV), z-scoring, domain adaptation methods, etc.

Classification and some other functions can run in parallel to harness the power of multiple cores.

Includes sleep analyses:Detects slow oscillations (SOs), sleep spindles, theta activity and other activities that can be provided in a custom file, thus it could detect new activities/patterns if their specifications are provided.

Includes statistical analyses:Provides correction for multiple comparisons in 1d and 2d with cluster-based permutation tests and also correction in space. Includes parametric and non-parametric tests for significance. Visualises correlations with statistics. Visualises statistical results with p-values.

Calls some functions from fieldtrip toolbox, and some functions from few toolboxes that are relevant for some analyses.