Zero-Shot Neural Priors for Generalizable Cross-Subject and Cross-Task EEG Decoding
Baimam Boukar Jean Jacques, Brandone Fonya, Nchofon Tagha Ghogomu, Pauline Nyaboe, Kipngeno Koech
arXiv preprint · Signal Processing (eess.SP)
A zero-shot cross-subject framework for generalizable EEG decoding on the large-scale Healthy Brain Network dataset, benchmarking a CNN baseline, a hybrid LSTM, and a Transformer-based foundation model. To adapt the Transformer for regression without catastrophic forgetting, we propose a novel progressive unfreezing strategy. The fine-tuned Transformer reaches an nRMSE of 0.9799 on unseen subjects (vs. 0.9991 for the baseline), advancing scalable, calibration-free EEG decoding for computational psychiatry and behavioral prediction.
- Brain-Computer Interfaces
- EEG
- Foundation Models
Phoenix: Safe GitHub Issue Resolution via Multi-Agent LLMs
Kipngeno Koech, Muhammad Adam, Baimam Boukar Jean Jacques, Joao Barros
IEEE International Conference on Responsible Artificial Intelligence (IRAI), Melbourne, Australia
A multi-agent LLM system that resolves GitHub issues from triage through pull-request creation, combining seven layered safety controls with a baseline-aware test evaluation strategy. Work is decomposed across six specialized agents (planner, reproducer, coder, tester, failure analyst, and PR agent) coordinated by a label-based GitHub webhook state machine, with every change checked against a baseline test run before a PR is opened. Phoenix oracle-resolves 75% of a SWE-bench Lite slice with no pass-to-pass regressions, and preserves correctness on 100% of a 42-issue pilot across 14 repositories.
- Multi-Agent LLMs
- Software Engineering
- SWE-bench
Improving SSVEP BCI Spellers with Data Augmentation and Language Models
J. Zhang, R. Zhang, K. Koech, D. Hill, and K. Shapovalenko
IEEE Workshop on Signal Processing Systems (SiPS), Hong Kong
A hybrid framework integrating domain-specific EEG data augmentation with a language model to improve SSVEP-based Brain-Computer Interface speller accuracy for individuals with motor disabilities. It decodes scalp-recorded EEG to identify the characters a user gazes at, tackling high EEG variability and poor generalization to unseen subjects with an end-to-end PyTorch training and evaluation pipeline.
- Brain-Computer Interfaces
- EEG
- NLP
Phasor Measurement Integration in Wireless Power Transfer Systems for Grid Applications
C. Ogbogu, K. Koech, and J. Thornburg
IEEE International Microwave and Antenna Symposium (IMAS), Nairobi, Kenya
An investigation into integrating phasor measurement with wireless power transfer systems for grid applications, supporting real-time monitoring and more reliable power-grid operation.
- Power Systems
- Phasor Measurement
- Grid