BCI Software

EEG / BCI research engineering

These projects address one connected problem: an EEG model trained on one recording session cannot be assumed to remain reliable on the next. The software chain runs cross-session transfer experiments, diagnoses drift and adaptation outcomes, and tests trained decoders through sample-paced pseudo-online replay. Supporting libraries provide train/test-separated feature extraction, domain adaptation, simulation, and visual explanation.

01

Three complementary systems for cross-session transfer, drift diagnosis, and sample-paced pseudo-online evaluation.

CrossDA project cover

Cross-session DA experiment runner

CrossDA

Python package and CLI for running cross-session EEG domain-adaptation experiments across multiple source-session utilization strategies.

Evidence: CLI + Python API; MAP, DWP, MMP, and BDP; auditable summary, detail, and session-role outputs

Details GitHub
ShiftDx project cover

Drift diagnostics dashboard

ShiftDx

Streamlit dashboard for MI-EEG drift diagnostics across multiple shift metrics, DA methods, feature families, and fixed-reference monitoring protocols.

Evidence: Five drift metrics; fixed-reference monitoring; no-DA, DA, and retraining comparisons

Details GitHub Live Demo

Pseudo-online BCI desktop app

NeuroStream

Desktop app for sample-paced BCI replay with four motor-imagery pipelines, Euclidean Alignment, and live inference visualization.

Evidence: Four MI pipelines; 0.5 s progressive checkpoints; sample-paced pseudo-online replay

Details GitHub

02

Build stable representations

Train/test-separated whitening and feature extraction for comparable EEG trials.

eegwhiten project cover

EEG whitening and alignment package

eegwhiten

R package for whitening EEG channels or feature matrices with reusable models, diagnostics, cross-session recentering, and Riemannian tangent-space helpers.

Details GitHub
BCIFeatR project cover

EEG feature extraction package

BCIFeatR

R toolkit for train/test-safe EEG feature extraction across spatial-filter, covariance, spectral, dynamical, and avalanche feature families.

Details GitHub

03

Adapt across sessions

Reusable R and Python methods for alignment, transport, and source-target shift analysis.

DA4BCI project cover

Domain adaptation package

DA4BCI

R toolkit for aligning EEG feature distributions across sessions or subjects and evaluating source-target shift before and after adaptation.

Details GitHub
DA4BCI-Python project cover

Python domain adaptation package

DA4BCI-Python

Python backend for EEG/BCI domain adaptation, shift metrics, SPD geometry, preprocessing, and drift detection.

Details GitHub

04

Audit benchmark results

Interactive inspection of source-session roles, configuration sensitivity, and runtime tradeoffs.

MSDA-Bench project cover

Multi-source DA benchmark dashboard

MSDA-Bench

Interactive benchmark dashboard for comparing source-session utilization strategies across MAP, DWP, MMP, and BDP pipelines.

Details GitHub Live Demo

05

Stress-test and explain methods

Simulation and visual tools for covariance geometry, augmentation audit, and method intuition.

TensorEEG project cover

EEG simulation and augmentation audit package

TensorEEG

R package for synthetic EEG tensor simulation, SPD covariance augmentation, fidelity auditing, and manifest-based replay.

Details GitHub
TensorEEG-py project cover

Python simulation and augmentation audit package

TensorEEG-py

Python package mirroring TensorEEG's simulation, augmentation, SPD geometry, fidelity audit, and manifest replay APIs.

Details GitHub
ShiftLens project cover

Interactive DA method explorer

ShiftLens

Static browser app for teaching and inspecting domain-adaptation geometry with 2-D toy data, method animations, metrics, and export tools.

Details Live Demo

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Selected EEG Competition Work

Kaggle HMS - Harmful Brain Activity Classification, Silver Medal, rank 98 of 2,767 teams.