SSCN 2012 Abstracts


Full Papers
Paper Nr: 3
Title:

Fast BCI Calibration - Comparing Methods to Adapt BCI Systems for New Subjects

Authors:

Jean Thorey, Parvaneh Adibpour, Yohei Tomita, Antoine Gaume, Hovagim Bakardjian, Gérard Dreyfus and François-B. Vialatte

Abstract: A Brain Computer Interface (BCI) is a system where a direct connection is established between the brain and a computer, providing a subject with a new communication channel. Unfortunately, BCI have many drawbacks: signal recording is problematic, brain signatures are non reproducible from individual to individual, etc. A dependent-BCI prototype, the BrainPC project, was developed in the SIGMA laboratory. Electroencephalographic (EEG) signals collected by a BrainAmp amplifier in responses to flickering light stimuli (Steady State Visual Evoked Potentials) are converted into machine-readable commands. This system is coupled with a human-machine interface. We propose a solution for fast calibration of the automatic detection of SSVEP between experimental subjects. We tested different calibration methods; harmonic and electrode selections were shown to be the most efficient methods.

Paper Nr: 6
Title:

EEG Signal Analysis via a Cleaning Procedure based on Multivariate Empirical Mode Decomposition

Authors:

Esteve Gallego-Jutglà, Tomasz M. Rutkowski, Andrzej Cichocki and Jordi Solé-Casals

Abstract: Artifacts are present in most of the electroencephalography (EEG) recordings, making it difficult to interpret or analyze the data. In this paper a cleaning procedure based on a multivariate extension of empirical mode decomposition is used to improve the quality of the data. This is achieved by applying the cleaning method to raw EEG data. Then, a synchrony measure is applied on the raw and the clean data in order to compare the improvement of the classification rate. Two classifiers are used, linear discriminant analysis and neural networks. For both cases, the classification rate is improved about 20%.

Paper Nr: 7
Title:

Adaptive Smoothing Applied to fMRI Data

Authors:

M. Bartés-Serrallonga, J. M. Serra-Grabulosa, A. Adan, C. Falcón, N. Bargalló and J. Solé-Casals

Abstract: One problem of fMRI images is that they include some noise coming from many other sources like the heart beat, breathing and head motion artifacts. All these sources degrade the data and can cause wrong results in the statistical analysis. In order to reduce as much as possible the amount of noise and to improve signal detection, the fMRI data is spatially smoothed prior to the analysis. The most common and standardized method to do this task is by using a Gaussian filter. The principal problem of this method is that some regions may be under-smoothed, while others may be over-smoothed. This is caused by the fact that the extent of smoothing is chosen independently of the data and is assumed to be equal across the image. To avoid these problems, we suggest in our work to use an adaptive Wiener filter which smooths the images adaptively, performing a little smoothing where variance is large and more smoothing where the variance is small. In general, the results that we obtained with the adaptive filter are better than those obtained with the Gaussian kernel. In this paper we compare the effects of the smoothing with a Gaussian kernel and with an adaptive Wiener filter, in order to demonstrate the benefits of the proposed approach.

Paper Nr: 9
Title:

Early Alzheimer’s Disease Progression Detection using Multi-subnetworks of the Brain

Authors:

Jaroslav Rokicki, Hiyoshi Kazuko, Francois-Benoit Vialatte, Andrius Ušinskas and Andrzej Cichocki

Abstract: Alzheimer’s disease is neurodegenerative disorder believed to affect 24.3 million people worldwide. Proposed MRI based disease progression markers have shown ability to perform the classification between the Alzheimer’s Disease (AD), Mild Cognitive Impariment (MCI) and Normal Cognitive (NC) subjects. We exploited two approaches, first one is to use single sub-network volume as a feature, second to use a network of most discriminative sub-networks. Multi-feature approach showed improvement by 4.5% in AD/NC classification case, and 1.5 % in MCI/NC case. Study was summarized for 48 AD, 119 MCI and 66 NC subjects.

Paper Nr: 11
Title:

Rehabilitation through Brain Computer Interfaces - Classification and Feedback Study

Authors:

Arnau Espinosa, Rupert Ortner, Danut Irimia and Christoph Guger

Abstract: A Brain-Computer Interface (BCI) is a tool for reading and interpreting signals recorded directly from the user’s brain. Most brain-computer interfaces (BCI) are based on one of three types of electroencephalogram (EEG) signals: P300s, steady-state visually evoked potentials (SSVEPs), and event-related desynchronization (ERD). EEG is typically recorded non-invasively using active or passive electrodes mounted on the human scalp. In recent years, a variety of different BCIs for communication and control applications were developed. A quite new and promising idea is to utilize BCIs as a tool for stroke rehabilitation. The BCI detects the user's movement intention and provides online feedback to train the affected parts of the body to restore effective movement. This publication tries to optimize current BCI-strategies for stroke rehabilitation using immersive 3-D virtual reality feedback (VRFB). Other work has continued to show that higher density electrode systems can reveal subtleties of brain dynamics that are not obvious with fewer electrodes. Hence, we used a larger electrode montage than typical BCI studies.

Paper Nr: 13
Title:

Alzheimer Disease Diagnosis based on Automatic Spontaneous Speech Analysis

Authors:

K. López de Ipiña, J. B. Alonso, J. Solé-Casals, N. Barroso, M. Faundez, M. Ecay, C. Travieso, A. Ezeiza and A. Estanga

Abstract: Alzheimer’s disease (AD) is the most prevalent form of progressive degenerative dementia and it has a high socio-economic impact in Western countries, therefore is one of the most active research areas today. Its diagnosis is sometimes made by excluding other dementias, and definitive confirmation must be done trough a post-mortem study of the brain tissue of the patient. The purpose of this paper is to contribute to im-provement of early diagnosis of AD and its degree of severity, from an automatic analysis performed by non-invasive intelligent methods. The methods selected in this case are Automatic Spontaneous Speech Analysis (ASSA) and Emotional Temperature (ET), that have the great advantage of being non invasive, low cost and without any side effects.

Short Papers
Paper Nr: 2
Title:

Facing the Challenge of Estimating Human Brain White Matter Pathways

Authors:

Adelino R. Ferreira da Silva

Abstract: Diffusion anisotropy has been used to characterize white matter neuronal pathways in the human brain, and infer global connectivity in the central nervous system. However, mapping complex fiber configurations in vivo remains a challenging task. We present a new methodology to reduce uncertainty in estimating the orientation of neuronal pathways in high angular resolution diffusion imaging (HARDI) reconstructions. The methodology relies on three main features. First, an optimized HARDI reconstruction technique based on the generalized q-sampling imaging approach is adopted. Second, directional statistics are used to estimate orientation distribution function (ODF) profile directions from data distributed on the unit sphere. Third, a modified streamline algorithm able to accommodate multiple fiber tracts and multiple orientations per voxel is used, to exploit the directional information gathered from estimated ODF profiles. The methodology has been tested on synthetic data simulations of crossing fibers and on a real data set.

Paper Nr: 4
Title:

Time Window Selection for Improving Error-related Potential Detection

Authors:

Rousseau Sandra, Jutten Christian and Congedo Marco

Abstract: In this paper we present an experiment enabling the occurence of the error-related potential in high cognitive load conditions and observe its inter-subject latency variability. We study the single-trial classification of the error-related potential using spatial filtering. Then we present a new adaptive algorithm for spatial filtering and time window selection that allows to adapt to error-related potential latency variability and provides better classification results.

Paper Nr: 5
Title:

Analysis of Spontaneous MEG Activity in Mild Cognitive Impairment using the Wavelet Turbulence

Authors:

Jesús Poza, Carlos Gómez, María García, Alberto Fernández and Roberto Hornero

Abstract: Mild cognitive impairment (MCI) is usually considered a pre-clinical stage of Alzheimer’s disease (AD). An appropriate characterization of MCI is crucial to achieve an early diagnosis of AD. Over the last few years, much effort has been devoted to identifying new diagnostic tests; tough further research is still required. In this study, we analyzed the spontaneous magnetoencephalographic (MEG) activity from 18 MCI subjects and 27 healthy controls to characterize the irregularity patterns in MCI. For that purpose, the wavelet turbulence (WT) was calculated from the time-scale representation provided by the continuous wavelet transform (CWT). Our results revealed that the mean values and the standard deviation of WT for MCI subjects were significantly higher and lower (p < 0.05) than for controls, respectively. These findings support the notion that MCI is associated with a significant decrease in irregularity and variability when compared to normal aging. A Receiver Operating Characteristic (ROC) analysis with a leave-one-out cross-validation procedure was applied to assess the diagnostic ability of WT. We obtained an accuracy of 66.7% and an area under ROC curve of 0.704. We conclude that the WT extends the concept of irregularity and provides potential descriptors of spontaneous MEG activity in MCI.

Paper Nr: 8
Title:

FPGA Implementation of SOBI to Perform BSS in Real Time

Authors:

Apurva Rathi, Xun Zhang and Francois Vialatte

Abstract: Blind Source Separation (BSS) is an effective and powerful tool for source separation and artifact removal in EEG signals. For the real time applications such as Brain Computer Interface (BCI) or clinical Neuro-monitoring, it is of prime importance that BSS is effectively performed in real time. The motivation to implement BSS in Field Programmable Gate Array (FPGA) comes from the hypothesis that the performance of the system could be significantly improved in terms of speed considering the optimal parallelism environment that hardware provides. In this paper, FPGA is used to implement the SOBI algorithm of EEG with a fixed-point algorithm. The results obtained show that, FPGA implementation of SOBI reduces the computation time and thus has great potential for real time.

Paper Nr: 10
Title:

Closed-looping a P300 BCI using the ErrP

Authors:

Rousseau Sandra, Jutten Christian and Congedo Marco

Abstract: The error-related potential is an event-related potential that gives information on the quality (error or correct) of what a subject observes. In this paper we try to integrate it in a P300 BCI system in order to introduce a closed-loop in this system and thus to improve its accuracy. We propose and compare different strategies of integration and discuss on their possible improvements depending on our system characteristics. We get a mean improvement of 10% of our system when using the error-related potential to correct errors.

Paper Nr: 12
Title:

EEG Beta Range Dynamics and Emotional Judgments of Face and Voices

Authors:

K. Hiyoshi-Taniguchi, M. Kawasaki, T. Yokota, H. Bakardjian, H. Fukuyama, F. B. Vialatte and A. Cichocki

Abstract: The purpose of this study is to clarify multi-modal brain processing related to human emotional judgment. This study aimed to induce a controlled perturbation in the emotional system of the brain by multi-modal stimuli, and to investigate whether such emotional stimuli could induce reproducible and consistent changes in the brain dynamics. As we were especially interested in the temporal dynamics of the brain responses, we studied EEG signals. We exposed twelve subjects to auditory, visual, or combined audio-visual stimuli. Audio stimuli consisted of voice recordings of the Japanese word ‘arigato’ (thank you) pronounced with three different intonations (Angry - A, Happy - H or Neutral - N). Visual stimuli consisted of faces of women expressing the same emotional valences (A, H or N). Audio-visual stimuli were composed using either congruent combinations of faces and voices (e.g. H x H) or non-congruent (e.g. A x H). The data was collected with a 32-channel Biosemi EEG system. We report here significant changes in EEG power and topographies between those conditions. The obtained results demonstrate that EEG could be used as a tool to investigate emotional valence and discriminate various emotions.