Enhanced Low-Latency Detection of Motor Intention From EEG for Closed-Loop Brain-Computer Interface Applications

• Published in: IEEE transactions on biomedical engineering 2014-02, Vol.61 (2), p.288-296
• Main Authors: Xu, Ren, Jiang, Ning, Lin, Chuang, Mrachacz-Kersting, Natalie, Dremstrup, Kim, Farina, Dario
• Format: Article
• Language: English
• Subjects: Accuracy; Brain-computer interface; Brain-Computer Interfaces; Devices; Discriminant analysis; Educational institutions; Electrodes; electroencephalogram (EEG); Electroencephalography; Electroencephalography - methods; Electromyography; Evoked Potentials, Motor - physiology; Human-computer interface; Humans; Locality Preserving Projection; Matched filters; motor intention; Motors; movement-related cortical potentials (MRCP); On-line systems; Online; Signal Processing, Computer-Assisted; Testing; Training
• ISSN: 0018-9294; 1558-2531
• DOI: 10.1109/TBME.2013.2294203

In recent years, the detection of voluntary motor intentions from electroencephalogram (EEG) has been used for triggering external devices in closed-loop brain-computer interface (BCI) research. Movement-related cortical potentials (MRCP), a type of slow cortical potentials, have been recently used for detection. In order to enhance the efficacy of closed-loop BCI systems based on MRCPs, a manifold method called Locality Preserving Projection, followed by a linear discriminant analysis (LDA) classifier (LPP-LDA) is proposed in this paper to detect MRCPs from scalp EEG in real time. In an online experiment on nine healthy subjects, LPP-LDA statistically outperformed the classic matched filter approach with greater true positive rate (79 ± 11% versus 68 ± 10%; p = 0.007) and less false positives (1.4 ± 0.8/min versus 2.3 ± 1.1/min; p = 0.016). Moreover, the proposed system performed detections with significantly shorter latency (315 ± 165 ms versus 460 ± 123 ms; p = 0.013), which is a fundamental characteristics to induce neuroplastic changes in closed-loop BCIs, following the Hebbian principle. In conclusion, the proposed system works as a generic brain switch, with high accuracy, low latency, and easy online implementation. It can thus be used as a fundamental element of BCI systems for neuromodulation and motor function rehabilitation.