Towards a cure for BCI illiteracy: machine learning based co-adaptive learning

Brain-Computer Interfaces (BCIs) allow a user to control a computer application by brain activity as acquired, e.g., by EEG. One of the biggest challenges in research is to understand and solve the problem of BCI Illiteracy, which is that control does not work for a non-negligible portion of subjects (estimated 15% to 30%). In a screening study, N = 80 subjects performed motor imagery first in a calibration (i.e. without feedback) measurement and then in a feedback measurement in which they could control a 1D cursor application. Coarsely, we observed three categories of subjects: subjects for whom (I) a classifier could be successfully trained and who performed feedback with good accuracy; (II) a classifier could be successfully trained, but feedback did not work well; (III) no classifier with acceptable accuracy could be trained. While subjects of Category II had obviously difficulties with the transition from offline on online operation, subjects of Category III did not show the expected modulation of sensorimotor rhythms (SMRs): either no SMR idle rhythm was observed over motor areas, or this idle rhythm was not attenuated during motor imagery. Here we present preliminary results of a pilot study in which it was investigated, whether co-adaptive learning using machine-learning techniques could help subjects of Categories II and III to achieve successful feedback. In this setup, the session immediately started with feedback. In the first three runs, a pretrained subject-independent classifier on simple features (band-power in alpha (8–15 Hz) and beta (16–32 Hz) frequency range in 3 Laplacian channels at C3, Cz, C4) was used and adapted (covariance matrix and pooled mean 1) to the subject after each trial. For the subsequent three runs, a classifier was trained on a more complex composed band-power feature in a subject-specific narrow band from optimized Common Spatial Pattern (CSP 2) filters and in six Laplacian channels. While CSP filters were static, the position of the Laplacians was updated based on a statistical criterion and the classifier was retrained on the combined CSP plus Laplacians feature to provide flexibility with respect to spatial location of modulated brain activity. Finally for the last two runs, a classifier was trained on CSP features, which have been calculated on runs 4–6. The pooled mean of the linear classifier was adapted after each trial 1. Initially, we verified the novel experimental design with 6 subjects of Category I. Here, very good feedback performance was obtained within the first run after 20 to 40 trials of adaptation and further increased in subsequent runs. In the present pilot study, further two subjects of Category II and three of Category III took part. All these five subjects did not have control in the first three runs but they were able to gain it when the machine learning based techniques came into play in runs 4–6 (a jump from run 3 to run 4 in Cat. II, and a continuous increase in runs 4 to 6 in Cat. III; see Figure 1). For most subjects a further improvement could be obtained in runs 7 and 8. Summarizing, it was demonstrated that subjects categorized as illiterates before could gain control within one session. In particular, one subject who had no SMR idle rhythm in the from Eighteenth Annual Computational Neuroscience Meeting: CNS*2009 Berlin, Germany. 18–23 July 2009