Thomas Navin Lal

We consider the general problem of learning from labeled and unlabeled data, which is often called semi-supervised learning or transductive inference. A principled approach to semi-supervised learning is to design a classifying function which is sufficiently smooth with respect to the intrinsic structure collectively revealed by known labeled and unlabeled points. We present a simple algorithm to obtain such a smooth solution. Our method yields encouraging experimental results on a number of classification problems and demonstrates effective use of unlabeled data. 1

During the last ten years there has been growing interest in the development of Brain Computer Interfaces (BCIs).\nThe field has mainly been driven by the needs of completely paralyzed patients to communicate.\nWith a few exceptions, most human BCIs are based on extracranial electroencephalography (EEG).\nHowever, reported bit rates are still low. One reason for this is the low signal-to-noise ratio of the EEG.\nWe are currently investigating if BCIs based on electrocorticography (ECoG) are a viable alternative.\nIn this paper we present the method and examples of intracranial EEG recordings of three epilepsy patients\nwith electrode grids placed on the motor cortex. The patients were asked to repeatedly imagine movements of two kinds,\ne.g., tongue or finger movements. We analyze the classifiability of the data using Support Vector Machines (SVMs) and Recursive Channel Elimination (RCE).

Most EEG-based brain-computer interface (BCI) paradigms come along with specific electrode positions, for example, for a visual-based BCI, electrode positions close to the primary visual cortex are used. For new BCI paradigms it is usually not known where task relevant activity can be measured from the scalp. For individual subjects, Lal et al. in 2004 showed that recording positions can be found without the use of prior knowledge about the paradigm used. However it remains unclear to what extent their method of recursive channel elimination (RCE) can be generalized across subjects. In this paper we transfer channel rankings from a group of subjects to a new subject. For motor imagery tasks the results are promising, although cross-subject channel selection does not quite achieve the performance of channel selection on data of single subjects. Although the RCE method was not provided with prior knowledge about the mental task, channels that are well known to be important (from a physiological point of view) were consistently selected whereas task-irrelevant channels were reliably disregarded.

Motivated by the particular problems involved in communicating with "locked-in" paralysed patients, we aim to develop a brain-computer interface that uses auditory stimuli. We describe a paradigm that allows a user to make a binary decision by focusing attention on one of two concurrent auditory stimulus sequences. Using Support Vector Machine classification and Recursive Channel Elimination on the independent components of averaged event-related potentials, we show that an untrained user's EEG data can be classified with an encouragingly high level of accuracy. This suggests that it is possible for users to modulate EEG signals in a single trial by the conscious direction of attention, well enough to be useful in BCI.