Using Rest Class and Control Paradigms for Brain Computer Interfacing

Using Rest Class and Control Paradigms for Brain Computer Interfacing Siamac Fazli1 , M´ arton Dan´ oczy1 , Florin Popescu2 , Benjamin Blankertz1,2 , and Klaus-Robert M¨ uller1 1

Berlin Institute of Technology, Machine Learning group, Franklinstr. 28/29, Berlin 2 IDA group, Fraunhofer FIRST, Kekul´estr. 7, Berlin

Abstract. The use of Electro-encephalography (EEG) for Brain Computer Interface (BCI) provides a cost-efficient, safe, portable and easy to use BCI for both healthy users and the disabled. This paper will first briefly review some of the current challenges in BCI research and then discuss two of them in more detail, namely modeling the “no command” (rest) state and the use of control paradigms in BCI. For effective prosthetic control of a BCI system or when employing BCI as an additional control-channel for gaming or other generic man machine interfacing, a user should not be required to be continuously in an active state, as is current practice. In our approach, the signals are first transduced by computing Gaussian probability distributions of signal features for each mental state, then a prior distribution of idle-state is inferred and subsequently adapted during use of the BCI. We furthermore investigate the effectiveness of introducing an intermediary state between state probabilities and interface command, driven by a dynamic control law, and outline the strategies used by 2 subjects to achieve idle state BCI control.

1

Introduction

Non-invasive Brain Computer Interfacing (BCI) has recently become a hot topic with research activities outside its traditional fields medicine, psychology, neuroscience and rehabilitation engineering [11,37,18]. As many novel applications beyond rehabilitation have emerged [23,11,17] also other disciplines such as computer science have started to contribute with novel signal processing, machine learning, software and man machine interaction concepts [3]. Furthermore novel sensors, amplifiers and open source software [30,32] have increased the ease of handling BCIs and have therefore lowered the overall threshold for new groups to enter this highly interdisciplinary field of neurotechnology. In particular employing machine learning techniques allows a successful BCI communication for novices even from the first session [2,4]: instead of several hundred hours of subject training now the machine learns to decode the brain states of BCI users individually [2,4,11]. This concept of ’letting the machine learn’ (instead of the subjects) was introduced by the Berlin Brain Computer Interface, adapting feature extraction and classification to data acquired in a so-called brief calibration phase (less than 5 minutes) where the subject is focussing to reproducibly generate certain brain states, e.g. imagery movements. The learning machine computes J. Cabestany et al. (Eds.): IWANN 2009, Part I, LNCS 5517, pp. 651–665, 2009. c Springer-Verlag Berlin Heidelberg 2009 

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a statistical estimator from this calibration data which then allows to discriminate these learned brain states during the feedback part of the experiment where the subject can communicate with the machine by power of though only (see [4,15]). The practical versatility of BCI – this novel additional modality of interaction between man and machine – is yet far from explored [11,23,3,17]. Note however that despite its bright future perspectives EEG based BCI faces a number of challenges that we would like to discuss following [25,11]. 1.1

Challenges in BCI

First of all, the information transfer rate (ITR) achievable through EEG is approximately one order of magnitude lower than the one observed by invasive methods in monkey studies [29,24,34]. That said, the potential benefits of brain implant based BCI has so far not been demonstrated to be worth the associated cost and risk in the most disabled of patients, let alone in healthy users [13]. EEG seems for now the only practical brain-machine interaction (BMI) choice (cost and ITR limitations hamper other non-invasive methods). The most elementary of EEG-BCI challenges for healthy users is not – at first glance – a computational one. Standard EEG practice involves the tedious application of conductive gel on EEG electrodes in order to provide for accurate measurements of the micro-volt level scalp potentials that constitute EEG signals. Without “dry-cap” technology the proper set-up of BCI sessions in, say, a home environment, is too tedious, messy and therefore impractical. Marketing promises of impending “dry-cap” EEG have already made some media impact, while we have also presented “dry-cap” EEG-BCI design and performance in a controlled study [28]. All foreseeable systems, for reasons of ease-of-use and cost, use fewer electrodes than found on standard EEG caps. The computational challenges which we have addressed are (1) optimal placement of the reduced number of electrodes and (2) robustness of BCI algorithms to the smaller set of recording sites. With only 6 uni-polar electrodes we can achieve about 70% of full gel cap BCI performance at sites above the motor cortex, while being able to discount any potential influence of muscle and eye movement artifacts. Most other dry-cap challenges remaining are of an engineering design nature, excluding perhaps the computational reduction of artifacts produced not by unrelated electro-physiological activity but by measured low-frequency voltage variations caused by the physical movement of the head. A long-standing problem of BCI designs which detect EEG patterns related to some voluntarily produced brain state is that such paradigms work with varying success among subjects/patients. We distinguish mental task based BCI such as “movement imagination” BCI from paradigms based on involuntary stimulus related potentials such as P300 which are limited to very specific applications such as typing for locked-in patients and require constant focus on stimuli extraneous to the task at hand. The peak performance to be achieved even after multiple sessions, varies greatly among subjects. Using a recent study [6] and other unreported data by many research groups, we estimate that about 20%

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of subjects do not show strong enough motor related mu-rhythm variations for effective asynchronous motor imagery BCI, that for another 30% performance is slow (20 bits/min, is achievable within the next few years. Clearly, challenges as the ones discussed above need to be met, in order to bring EEG BCI technology closer to becoming a commonplace computer peripheral. Acknowledgments. The studies were partly supported by BFNT, BMBF FKZ 01IBE01A/B, by DFG MU 987/3-1 and by the EU under PASCAL2. This publication only reflects the authors’ views.

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