Gangadhar Garipelli

The use of brain-computer interfacing for ambient intelligence

This book constitutes the refereed proceedings of the workshops of the First European Conference on Ambient Intelligence, AmI 2007, held in Darmstadt, Germany, in November 2007. The papers are organized in topical sections on AI methods for ambient intelligence, evaluating ubiquitous systems with users, model driven software engineering for ambient intelligence applications, smart products, ambient assisted living, human aspects in ambient intelligence, Amigo, WASP as well as the cojoint PERSONA and SOPRANO workshops and the KDubiq workshop.

Single trial analysis of slow cortical potentials: a study on anticipation related potentials

OBJECTIVE Abundant literature suggests the use of slow cortical potentials (SCPs) in a wide spectrum of basic and applied neuroscience areas. Due to their low signal to noise ratio, these potentials are often studied using grand-average analysis, which conceals trial-to-trial information. Moreover, most of the single trial analysis methods in the literature are based on classical electroencephalogram (EEG) features ([1-30] Hz) and are likely to be unsuitable for SCPs that have different signal properties (such as having the signals spectral content in the range [0.2-0.7] Hz). In this paper we provide insights into the selection of appropriate parameters for spectral and spatial filtering. APPROACH We study anticipation related SCPs recorded using a web-browser application protocol and a full-band EEG (FbEEG) setup from 11 subjects on two different days. MAIN RESULTS We first highlight the role of a bandpass with [0.1-1.0] Hz in comparison with common practices (e.g., either with full dc, just a lowpass, or with a minimal highpass cut-off around 0.05 Hz). Secondly, we suggest that a combination of spatial-smoothing filter and common average reference (CAR) is more suitable than the spatial filters often reported in the literature (e.g., re-referencing to an electrode, Laplacian or CAR alone). Thirdly, with the help of these preprocessing steps, we demonstrate the generalization capabilities of linear classifiers across several days (AUC of 0.88 ± 0.05 on average with a minimum of 0.81 ± 0.03 and a maximum of 0.97 ± 0.01). We also report the possibility of further improvements using a Bayesian fusion technique applied to electrode-specific classifiers. SIGNIFICANCE We believe the suggested spatial and spectral preprocessing methods are advantageous for grand-average and single trial analysis of SCPs obtained from EEG, MEG as well as for electrocorticogram. The use of these methods will impact basic neurophysiological studies as well as the use of SCPs in the design of neuroprosthetics.

Discriminative channel selection method for the recognition of anticipation related potentials from CCD estimated cortical activity

Recognition of brain states and subjects intention from electroencephalogram (EEG) is a challenging problem for braincomputer interaction. Signals recorded from each of EEG electrodes represent noisy spatio-temporal overlapping of activity arising from very diverse brain regions. However, un-mixing methods such as Cortical Current Density (CCD) can be used for estimating activity of different brain regions. These methods not only improve spatial resolution but also signal to noise ratio, hence the classifiers computed using this activity may ameliorate recognition performances. However, these methods lead to a multiplied number of channels, leading to the question - “How to choose relevant and discriminant channels from a large number of channels?”. In the current paper we present a channel selection method and discuss its application to the recognition of anticipation related potentials from surface EEG channels and CCD estimated cortical potentials. We compare the classification accuracies with previously reported performances obtained using Cz electrode potentials of 9 subjects (3 experienced + 6 naïve). As hypothesized, we observed improvements for most subjects with channel selection method applied to CCD activity as compared to surface-EEG channels and baseline performances. This improvement is particularly significant for subjects who are naïve and did not show a clear pattern on ERP grand averages.

Brain-computer interfaces for post-stroke motor rehabilitation: a meta-analysis

Brain‐computer interfaces (BCIs) can provide sensory feedback of ongoing brain oscillations, enabling stroke survivors to modulate their sensorimotor rhythms purposefully. A number of recent clinical studies indicate that repeated use of such BCIs might trigger neurological recovery and hence improvement in motor function. Here, we provide a first meta‐analysis evaluating the clinical effectiveness of BCI‐based post‐stroke motor rehabilitation. Trials were identified using MEDLINE, CENTRAL, PEDro and by inspection of references in several review articles. We selected randomized controlled trials that used BCIs for post‐stroke motor rehabilitation and provided motor impairment scores before and after the intervention. A random‐effects inverse variance method was used to calculate the summary effect size. We initially identified 524 articles and, after removing duplicates, we screened titles and abstracts of 473 articles. We found 26 articles corresponding to BCI clinical trials, of these, there were nine studies that involved a total of 235 post‐stroke survivors that fulfilled the inclusion criterion (randomized controlled trials that examined motor performance as an outcome measure) for the meta‐analysis. Motor improvements, mostly quantified by the upper limb Fugl‐Meyer Assessment (FMA‐UE), exceeded the minimal clinically important difference (MCID=5.25) in six BCI studies, while such improvement was reached only in three control groups. Overall, the BCI training was associated with a standardized mean difference of 0.79 (95% CI: 0.37 to 1.20) in FMA‐UE compared to control conditions, which is in the range of medium to large summary effect size. In addition, several studies indicated BCI‐induced functional and structural neuroplasticity at a subclinical level. This suggests that BCI technology could be an effective intervention for post‐stroke upper limb rehabilitation. However, more studies with larger sample size are required to increase the reliability of these results.

Analysis of Anticipation Related Potentials for Brain Computer Interaction

Anticipation is a mental process during which a person actively engages in a phase required for the sensory perception and execution of the optimal actions at the arrival of the relevant future events. Since this process occurs before the execution of an intended action, it may be used as a control signal for Brain Computer Interface (BCI) applications. Recognition of neural correlates of this process can enhance the performance of a BCI and in turn reduce mental workload of its users. To this end, it is vital to understand the neural correlates involved in this process and to design robust methods for its recognition in single trials. The analysis of these correlates may also contribute to the basic knowledge of the mechanisms underlying this behavior. The thesis provides three major contributions: (i) it reports methods for the robust recognition of anticipation related Electroencephalogram (EEG) potentials (ii) it provides insights into the selection of appropriate preprocessing steps required for enhancing the Signal-to-Noise Ratio (SNR) of anticipatory slow cortical potentials (SCPs) and (iii) it identifies scalp area specific oscillatory activity related to different aspects of anticipatory behavior. First, we focus on methods for the single trial recognition of anticipatory SCPs using the widely known classical contingent negative variation (CNV) paradigm. Using this paradigm, we demonstrate the feasibility of recognizing the anticipatory SCPs (CNV potentials) using features thatmodel its temporal pattern. We propose a Bayesian approach that exploits temporal evolution and redundancy to quickly classify (e.g., within half of the anticipatory period), without compromising classification accuracy. We then improve upon these recognition rates by using a source localization technique based on the biophysical model of the human head. We further validate the feasibility of recognizing CNV potentials in an online experiment, and report for the first time that, under controlled conditions, these potentials can be reproduced and recognized in realistic interaction scenarios (assistive technology web-browsing) with high accuracies. Second, the thesis provides insights into the selection of appropriate preprocessing stages required for improving the SNR of SCPs. The CNV potentials are characterized by low frequencies that are usually recorded with full-DC, and hence suffer from task-irrelevant high amplitude fluctuations and spatial noise. To account for this, we identified appropriate spectral and spatial filters to improve the SNR. We demonstrate the potential of these preprocessing stages by using fusing multiple electrode specific linear classifiers, which achieve recognition performances of 90±2% (area under curve of receiver operating characteristic), where the classifiers are trained using recordings from one day and tested on the recordings from several days apart. Finally, the thesis identifies different facets of anticipatory behavior. Apart from the widely known CNV potentials, it is not clear which other spectral bands could be related to anticipatory behavior. Using recordings from an experiment (i.e., the assistive technology web browser) where multiple warning stimuli predicted an imperative stimulus, we explored the phase and amplitude response of various oscillatory sub-bands for the identification of markers that could be associated with different aspects of anticipation. From this study we report that: (i) there are duration (4-10 seconds) specific changes in Electroencephalography (EEG) activity in the range 0-1 Hz in the central electrodes correlate with reaction time, (ii) there exist slow oscillations (0.1-1 Hz) in the central electrodes that exhibit phase tuning up to 4 seconds before the onset of a target cue, (iii) there are delta oscillations (1.5-3 Hz), which are entrained to predictive rhythmic warning cues, (iv) there is a selective modulation (increase or decrease) in the amplitude of occipital alpha band (8-12 Hz) based on the relevance of forthcoming visual cue, and (v) there exist a reduction in the beta band (14-30 Hz) amplitude in the sensory motor and association areas lasting up to 10 seconds. The phase tuning and entrainment resulted in a low variance of phase values at the arrival of the imperative stimulus, which may be required for its optimal processing. The amplitude modulation of alpha band activity is likely to be a resultant of sensory suppression and attention. The reduction of beta-band activity over long periods of time suggests holding of sensory-motor association areas until the execution of a planned action. We believe that these observations are the consequence of the endogenous drive on the ongoing oscillations to enhance the processing of the forthcoming stimuli and preparation for an intended action. In summary, the thesis provides methods for the recognition of anticipatory SCPs by exploiting spectral and spatio-temporal characteristics with high performances. By exploring various other oscillatory sub-bands, the spectral characteristics of different aspects of anticipatory behavior are also identified. Further, methods modeling these characteristics can bring forth more robust and faster techniques for BCI systems.