Securing EEG-based Brain-Computer Interface Systems from Data Poisoning Attacks

Electroencephalogram (EEG)-based brain computer interface (BCI) is a widely used access technology to aid human-computer interactions. It enables communication between the human brain and external devices directly without the need for actuators such as human hands and legs. The BCI system acquires brain signals from an EEG device and uses machine learning (ML) algorithms to analyze and interpret the signals into actionable commands. However, EEG-based BCI systems are vulnerable to data poisoning attacks, which compromises the accuracy and security of the BCI system, and user safety. The objective of this paper is to protect the BCI systems against backdoor data poisoning attacks for reliable system operations. In this paper, a backdoor detect-and-clean mechanism, code named Bkd-DETCLEAN, to secure EEG-based BCI systems against data poisoning (backdoor) attacks is proposed using the Random Forest Classifier. Two models were designed, trained and validated on both clean and poisoned dataset respectively. The results of experiments on two benchmark EEG datasets shows that our solution achieves a detection accuracy of 98.5%, effectively identifying poisoned samples with a little below 5% false positive rate. Continued data cleaning iterations restored the poisoned training set, resulting in an overall system accuracy improvement from 78.9% to 93%. Based on these results, the proposed model sustained high detection and cleaning efficiency with different poisoning rates, underscoring the effectiveness of the machine learning driven proposed model in ensuring that brain signal integrity is not compromised. The proposed mechanism is also applicable in other areas including healthcare and medical data protection, protecting fraud detection models in financial systems, ensuring the integrity of sensor data in industrial control systems, protecting against user data manipulation in recommender systems, etc.