EEG) Based Neurofeedback Training for Brain- Computer Interface (BCI)

Title Electroencephalography (EEG) based neurofeedback training for brain- computer interface (BCI) Kyuwan Choi Rutgers University Psychology Department; Computational Biomedicine Imaging and Modeling, Computer Science Rutgers University Psychology Department (Busch Campus) 152 Frelinghuysen Rd. Piscataway, NJ 08854 1 Abstract Electroencephalography (EEG) has become a popular tool in basic brain research, but in recent years several practical limitations have been highlighted. Some of the drawbacks pertain to the offline analyses of the neural signal that prevent the subjects from engaging in real-time error correction during learning. Other limitations include the complex nature of the visual stimuli, often inducing fatigue and introducing considerable delays, possibly interfering with spontaneous performance. By replacing the complex external visual input with internally driven motor imagery we can overcome some delay problems, at the expense of losing the ability to precisely parameterize features of the input stimulus. To address these issues we here introduce a non-trivial modification to Brain Computer Interfaces (BCI). We combine the fast signal processing of motor imagery with the ability to parameterize external visual feedback in the context of a very simple control task: attempting to intentionally control the direction of an external cursor on command. By engaging the subject in motor imagery while providing real- time visual feedback on their instantaneous performance, we can take advantage of positive features present in both externally- and internally-driven learning. We further use a classifier that automatically selects the cortical activation features that most likely maximize the performance accuracy. Under this closed loop co-adaptation system we saw a progression of the cortical activation that started in sensory-motor areas, when at chance performance motor imagery was explicitly used, migrated to BA6 under deliberate control and ended in the more frontal regions of prefrontal cortex, when at maximal performance accuracy, the subjects reportedly developed spontaneous mental control of the instructed direction. We discuss our results in light of possible applications of this simple BCI paradigm to study various cognitive phenomena involving the deliberate control of a directional signal in decision making tasks performed with intent. Index terms- EEG, Brain plasticity, Neurofeedback training, Motor imagery 2 Introduction Current experimental paradigms using electroencephalography (EEG) as a tool for basic research in cognitive and psychological sciences suffer from a speed and accuracy tradeoff (Stomrud et al. 2010;Liu et al. 2008). The use of external visual signals has several advantageous features. One of them is the ability to harness Evoked Related Potentials (ERP’s) in response to rich visual input of abstract cognitive content whereby the external input can be precisely parameterized to help infer correlations between the neural signal and the stimulus’ features. However visual signal processing is much too slow to allow for fast, spontaneous performance (Maunsell and Gibson 1992). The complex signal also induces eye strain, compounded with habituation effects. The latter are mostly due to long experimental sessions, necessary to average the noisy output over many repeats and extract the signal. An example of visually driven settings is the use of the P300 as steady state visually evoked potentials (SSVEPs). There, the ERP’s decrease over time and the quality of the signal to control an external device using BCI tends to degrade (Wolpaw et al. 2002; Serby et al. 2005; He et al. 2010). The habituation factor and the increase in the noise to signal ratios introduce considerable delays that reach timescales visual awareness. Such delays interfere with the subject’s ability to spontaneously adapt the neural-feedback signal and attain real time control of the external device. Experiments that use SSVEP have recognized that subjects also tend to feel eye fatigue and even admit to the risk of inducing epilepsy (Middendorf et al. 2000; Wang et al. 2006). Experimental paradigms using internally driven motor imagery offer some advantages over externally driven ERP’s (Choi 2012). Motor imagery does not require visual search, so eye straining is not a problem. Users can engage in motor imagery for long periods of time and the response signal is immediate. In this context the subjects are unaware of explicit delays between the imagined command and the performance outcome, both attained under latencies that would enable them to attempt the mental control of an external device in real time. However, the motor imagery paradigm also has some drawbacks: (1) accuracy is not as high because a large part of the internal stimuli has to be inferred and cannot be parameterized, (2) it takes longer to train the participant, and (3) a large number of participants cannot control a BCI device through motor imagery alone, even upon training over an extended period of time. Most BCI studies using motor imagery do offline analysis of the cortical activation with an eye for cortical regions that are well known for certain functions (Ramoser et al. 2000; Li et al. 2010). Because the analysis of the signal is off-line and pre-determined in phrenology-like fashion, the 3 participants have no way of knowing whether the motor imagery that they engaged into helped or hindered their performance. The experimenter can also potentially miss neural information from non-targeted cortical areas that may be of relevance for a given context. In general under such off-line and a priori determined settings subjects cannot engage in error-correction loops to try and adapt their motor imagery to eventually exert control over the external device. We propose in this study a simple paradigm that combines visual feedback from the subject’s real-time performance with motor imagery in closed loop so as to engage the subject in a co- adaptation process between the external performance and the internal cortical signal. By relying on the internally driven motor imagery we overcome the delays imposed by the visual processing of complex visual imagery. Motor imagery enables us to provide immediate on-line feedback of the performance based on 125ms sliding window of brain activation. Such a continuous, fast-processing window permits the spontaneous co-adaptation between the neural signal and the instantaneous visual feedback of the desired external cursor direction because the subject is unaware of processing and transmission delays, thus avoiding the types of interference that emerge in the abovementioned SSEVP setups. Lastly, although we cannot entirely parameterize the internal motor imagery, we can track the neural activation across all 64 channels and automatically select the features that maximize performance accuracy. In this way, instead of a priori harnessing the neural signal from pre-defined areas, we let a Bayesian sparse probit classifying algorithm automatically select the regions with highest activation to solve this specific intentional task. We discuss the results of employing this non-trivial modification of the BCI settings in the context of possible usage for future basic research in the cognitive and psychological sciences with translational value for clinical use. Materials and Methods Overview The EEG signal mainly reflects the superposition of the electrical activity created by the ionic charge oscillation due to postsynaptic potentials of neocortical pyramidal cells (Nunez 1995; Nunez and Silberstein 2000). Thus, a large population of neurons beneath an electrode is superimposed to the measureable EEG but relatively little spatial information can be derived from a single channel of EEG. To overcome these problems, studies estimating source currents from EEG channel signals have been steadily conducted in the BCI community. Congedo et al. (Congedo et al. 2004) constructed the first LORETA (low-resolution electromagnetic 4 tomography) tomographic neurofeedback system using source current estimation and conducted a study enhancing the low beta (16-20 Hz) and suppressing the low alpha (8-10 Hz) component of the anterior cingulate cognitive division (ACcd) which is related to attention process. Grave de Peralta Menendez et al. (Grave de Peralta Menendez et al. 2005) introduced a source reconstruction method into an existing motor imagery based BCI field. By using the ELECTRA inverse solution (Grave de Peralta Menendez et al. 2000), they demonstrated the utility of the source reconstruction. Qin et al. (Qin et al. 2004) used single-equivalent-dipole model, and Kamousi et al. (Kamousi et al. 2005) employed two-equivalent-dipole model to classify left and right hand motor imagery. They obtained about 80 % accuracy on it. Furthermore, the use of motor imagery proved useful when Noirhomme et al. (Noirhomme et al. 2008) reconstructed the sources of 400 dipoles and classified motor imagery using frequency power change and Bereitshaft potential as the features. In the present study, source currents over 2,240 vertexes were estimated from EEG signals of 64 channels through a hierarchical Bayesian method introducing a hierarchical prior (Sato et al. 2004; Batenburg et al. 1994) that can effectively incorporate both structural and functional MRI data. In this hierarchical Bayesian method, the variance of the source current at each source location is considered an unknown parameter and estimated from the observed EEG data. Prior information is

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