28 research outputs found
Human brain distinctiveness based on EEG spectral coherence connectivity
The use of EEG biometrics, for the purpose of automatic people recognition,
has received increasing attention in the recent years. Most of current analysis
rely on the extraction of features characterizing the activity of single brain
regions, like power-spectrum estimates, thus neglecting possible temporal
dependencies between the generated EEG signals. However, important
physiological information can be extracted from the way different brain regions
are functionally coupled. In this study, we propose a novel approach that fuses
spectral coherencebased connectivity between different brain regions as a
possibly viable biometric feature. The proposed approach is tested on a large
dataset of subjects (N=108) during eyes-closed (EC) and eyes-open (EO) resting
state conditions. The obtained recognition performances show that using brain
connectivity leads to higher distinctiveness with respect to power-spectrum
measurements, in both the experimental conditions. Notably, a 100% recognition
accuracy is obtained in EC and EO when integrating functional connectivity
between regions in the frontal lobe, while a lower 97.41% is obtained in EC
(96.26% in EO) when fusing power spectrum information from centro-parietal
regions. Taken together, these results suggest that functional connectivity
patterns represent effective features for improving EEG-based biometric
systems.Comment: Key words: EEG, Resting state, Biometrics, Spectral coherence, Match
score fusio
Multiscale Granger causality analysis by \`a trous wavelet transform
Since interactions in neural systems occur across multiple temporal scales,
it is likely that information flow will exhibit a multiscale structure, thus
requiring a multiscale generalization of classical temporal precedence
causality analysis like Granger's approach. However, the computation of
multiscale measures of information dynamics is complicated by theoretical and
practical issues such as filtering and undersampling: to overcome these
problems, we propose a wavelet-based approach for multiscale Granger causality
(GC) analysis, which is characterized by the following properties: (i) only the
candidate driver variable is wavelet transformed (ii) the decomposition is
performed using the \`a trous wavelet transform with cubic B-spline filter. We
measure GC, at a given scale, by including the wavelet coefficients of the
driver times series, at that scale, in the regression model of the target. To
validate our method, we apply it to publicly available scalp EEG signals, and
we find that the condition of closed eyes, at rest, is characterized by an
enhanced GC among channels at slow scales w.r.t. eye open condition, whilst the
standard Granger causality is not significantly different in the two
conditions.Comment: 4 pages, 3 figure
Application of cepstrum analysis and linear predictive coding for motor imaginary task classification
In this paper, classification of electroencephalography (EEG) signals of motor imaginary tasks is studied using cepstrum analysis and linear predictive coding (LPC). The Brain-Computer Interface (BCI) competition III dataset IVa containing motor imaginary tasks for right hand and foot of five subjects are used. The data was preprocessed by applying whitening and then filtering the signal followed by feature extraction. A random forest classifier is then trained using the cepstrum and LPC features to classify the motor imaginary tasks. The resulting classification accuracy is found to be over 90%. This research shows that concatenating appropriate different types of features such as cepstrum and LPC features hold some promise for the classification of motor imaginary tasks, which can be helpful in the BCI context
Graph Attention Based Spatial Temporal Network for EEG Signal Representation
Graph attention networks (GATs) based architectures have proved to be powerful at implicitly learning relationships between adjacent nodes in a graph. For electroencephalogram (EEG) signals, however, it is also essential to highlight electrode locations or underlying brain regions which are active when a particular event related potential (ERP) is evoked. Moreover, it is often im-portant to identify corresponding EEG signal time segments within which the ERP is activated. We introduce a GAT Inspired Spatial Temporal (GIST) net-work that uses multilayer GAT as its base for three attention blocks: edge atten-tions, followed by node attention and temporal attention layers, which focus on relevant brain regions and time windows for better EEG signal classification performance, and interpretability. We assess the capability of the architecture by using publicly available Transcranial Electrical Stimulation (TES), neonatal pain (NP) and DREAMER EEG datasets. With these datasets, the model achieves competitive performance. Most importantly, the paper presents atten-tion visualisation and suggests ways of interpreting them for EEG signal under-standing
User Identification and Verification from a Pair of Simultaneous EEG Channels Using Transform Based Features
In this study, the approach of combined features from two simultaneous Electroencephalogram (EEG) channels when a user is performing a certain mental task is discussed to increase the discrimination degree among subject classes, hence the visibility of using sets of features extracted from a single channel was investigated in previously published articles. The feature sets considered in previous studies is utilized to establish a combined set of features extracted from two channels. The first feature set is the energy density of power spectra of Discrete Fourier Transform (DFT) or Discrete Cosine Transform; the second one is the set of statistical moments of Discrete Wavelet Transform (DWT). Euclidean distance metric is used to accomplish feature set matching task. The combinations of features from two EEG channels showed high accuracy for the identification system, and competitive results for the verification system. The best achieved identification accuracy is (100%) for all proposed feature sets. For verification mode the best achieved Half Total Error Rate (HTER) is (0.88) with accuracy (99.12%) on Colorado State University (CSU) dataset, and (0.26) with accuracy (99.97%) on Motor Movement/Imagery (MMI) dataset