A real time classification algorithm for EEG-based BCI driven by self-induced emotions

Background and objective: The aim of this paper is to provide an efficient, parametric, general, and completely automatic real time classification method of electroencephalography (EEG) signals obtained from self-induced emotions. The particular characteristics of the considered low-amplitude signals (a self-induced emotion produces a signal whose amplitude is about 15% of a really experienced emotion) require exploring and adapting strategies like the Wavelet Transform, the Principal Component Analysis (PCA) and the Support Vector Machine (SVM) for signal processing, analysis and classification. Moreover, the method is thought to be used in a multi-emotions based Brain Computer Interface (BCI) and, for this reason, an ad hoc shrewdness is assumed. Method: The peculiarity of the brain activation requires ad-hoc signal processing by wavelet decomposition, and the definition of a set of features for signal characterization in order to discriminate different self-induced emotions. The proposed method is a two stages algorithm, completely parameterized, aiming at a multi-class classification and may be considered in the framework of machine learning. The first stage, the calibration, is off-line and is devoted at the signal processing, the determination of the features and at the training of a classifier. The second stage, the real-time one, is the test on new data. The PCA theory is applied to avoid redundancy in the set of features whereas the classification of the selected features, and therefore of the signals, is obtained by the SVM. Results: Some experimental tests have been conducted on EEG signals proposing a binary BCI, based on the self-induced disgust produced by remembering an unpleasant odor. Since in literature it has been shown that this emotion mainly involves the right hemisphere and in particular the T8 channel, the classification procedure is tested by using just T8, though the average accuracy is calculated and reported also for the whole set of the measured channels. Conclusions: The obtained classification results are encouraging with percentage of success that is, in the average for the whole set of the examined subjects, above 90%. An ongoing work is the application of the proposed procedure to map a large set of emotions with EEG and to establish the EEG headset with the minimal number of channels to allow the recognition of a significant range of emotions both in the field of affective computing and in the development of auxiliary communication tools for subjects affected by severe disabilities

Effects of the CPAP Treatment on the NON-REM Sleep Microstructures in Patients with Severe Apnea-Hypoapnea Syndrome

Sleep quality is affected in patients with sleep apnea- hypopnea syndrome (SAHS) with nocturnal and diurnal consequences. Most of these patients who are treated with positive airway pressure (CPAP) return to normal sleep patterns. We could consider good sleepers those patients who present more sleep spindles in stage II, and slower wave sleep as a good sign of better sleep quality. The objective in this research study was to compare the microstructure of stage II using the number of spindles and the increase of slow wave sleep before and after CPAP night titration. We developed a wavelet filter using a spline cubic function from a wavelet mother, which was appropriate to be used over electroencephalographic signal. By means of this filter in a multi-resolution mode, the spindles were detected from the increase of the IV band power; the sampling rate of the device determined the filter characteristics. The staging of polysomnographic studies was made by an expert according AASM (American Academy of Sleep Medicine) and then processed by the filter to get the index of sleep spindles before-and-after CPAP during stage II as well as the relationship between fast and slow powers from the EEG signal. An increase in the power of the slow waves vs. fast activity was observed in all the cases as a feature of better sleep. The neuroprotective effect described in previous research works regarding the density of the sleep spindles seems to be detected in patients improving their sleep quality after the correction of the apnea-hypopnea syndrome using CPAP.Fil: Smurra, Marcela. Gobierno de la Ciudad de Buenos Aires. Hospital General de Agudos Dr. Enrique Tornú; ArgentinaFil: Blanco, Susana Alicia Ana. Universidad de Belgrano. Facultad de Ingeniería; Argentina. Consejo Nacional de Investigaciones Científicas y Técnicas; ArgentinaFil: Eguiguren, Veronica. Gobierno de la Ciudad de Buenos Aires. Hospital General de Agudos Dr. Enrique Tornú; ArgentinaFil: Di Risio, Cecilia Diana. Universidad de Belgrano. Facultad de Ingeniería; Argentina. Consejo Nacional de Investigaciones Científicas y Técnicas; Argentin

Nonlinear denoising of transient signals with application to event related potentials

We present a new wavelet based method for the denoising of {\it event related potentials} ERPs), employing techniques recently developed for the paradigm of deterministic chaotic systems. The denoising scheme has been constructed to be appropriate for short and transient time sequences using circular state space embedding. Its effectiveness was successfully tested on simulated signals as well as on ERPs recorded from within a human brain. The method enables the study of individual ERPs against strong ongoing brain electrical activity.Comment: 16 pages, Postscript, 6 figures, Physica D in pres

Objective Quantification Of Selective Attention In Schizophrenia : A Hybrid TMS-EEG Approach

Schizophrenia is a brain disorder that exhibits effects on perception, way of thinking and behavior. Often, schizophrenia patients suffer from attention deficiency. Currently clinical interview is used to diagnose schizophrenia by doctors. There is no alternative way to diagnose schizophrenia in present. Thus, an objective approach by employing transcranial magnetic stimulation combined with electroencephalogram (TMS-EEG) is proposed. The aim of the study is to quantify objectively the neural correlate of selective attention that reflected in auditory late responses (ALRs) using signal processing techniques. TMS provides a means of stimulating neuronal structures within the cortex using brief time-varying magnetic pulses generated by a coil positioned over the scalp. Integrating it with electroencephalogram provides real-time information on cortical reactivity and connectivity through the analysis of TMS evoked potentials or induced oscillations. In this project, auditory oddball paradigm was used throughout the experiment. The experiment involved three sessions; 1) without TMS, 2) single pulse TMS (sTMS) and 3) repetitive TMS (rTMS). All sessions were conducted in attended (attention) and unattended (no attention) conditions. It is found that the amplitude of the grand averaged of ALR (the N1-P2 wave) is higher in control compared to schizophrenia in without TMS session at both conditions. However, the amplitude of ALR in schizophrenia subjects is higher than control subjects in sTMS and rTMS at both conditions. The attention level measure, i.e., the Wavelet Phase Stability (WPS) was used to extract and quantify the neural correlates of auditory selective attention reflected in ALRs. In particular, Complex Morlet was implemented (scales 50-100 corresponding to 4-8Hz). There are significant differences of the ALR between schizophrenia and control groups in without TMS (p<0.05) and sTMS at the attended condition (frontal electrodes). Meanwhile at the unattended condition, Significance difference is found between two groups of the subjects in without TMS but no significant difference in sTMS (frontal electrodes). Particularly, the WPS of controls are larger than schizophrenia patients for without TMS and sTMS at attended for frontal electrodes. These results were consistent for temporal electrodes. It is worth to note that the phase stability of ALR in single pulse TMS is lower than without TMS for controls during attended but showed reversed pattern in unattended. Besides, it is found that a large phase stability difference between without TMS and sTMS in schizophrenia (frontal and temporal electrodes) at unattended compared to attended. For control subjects, this difference is small at frontal and temporal electrodes in both conditions. In a further investigation, the C4.5 decision tree algorithm was implemented to classify the N1-P2 wave of control and schizophrenia subjects elicited by sTMS and rTMS. Four features (energy, power, variance and entropy) were extracted by continuous wavelet transform (CWT). The result shows high classification accuracy which is above 83% in all three sessions at both attended and unattended conditions. In conclusion, the combined TMS-EEG approach shows a promising way to study the selective attention in schizophrenia. By successfully quantifying the neural correlates of auditory selective attention reflected in ALRs using the WPS and discriminating the control and patient groups using C4.5 decision tree provides an objective way to diagnose schizophrenia in compliment to the current subjective method

Wavelet entropy of stochastic processes

We compare two different definitions for the wavelet entropy associated to stochastic processes. The first one, the Normalized Total Wavelet Entropy (NTWS) family [Phys. Rev. E 57 (1998) 932; J. Neuroscience Method 105 (2001) 65; Physica A (2005) in press] and a second introduced by Tavares and Lucena [Physica A 357 (2005)~71]. In order to understand their advantages and disadvantages, exact results obtained for fractional Gaussian noise (-1<alpha< 1) and the fractional Brownian motion (1 < alpha < 3) are assessed. We find out that NTWS family performs better as a characterization method for these stochastic processes.Comment: 12 pages, 4 figures, submitted to Physica

Effective electroencephalogram based epileptic seizure detection using support vector machine and statistical moment’s features

Epilepsy is one of the widespread disorders. It is a noncommunicable disease that affects the human nerve system. Seizures are abnormal patterns of behavior in the electricity of the brain which produce symptoms like losing consciousness, attention or convulsions in the whole body. This paper demonstrates an effective electroencephalogram (EEG) based seizure detection method using discrete wavelet transformation (DWT) for signal decomposition to extract features. An automatic channel selection method was proposed by the researcher to select the best channel from 23 channels based on maximum variance value. The records were segmented into a nonoverlapping segment with long 1-S. The support vector machine (SVM) model was used to automatically detect segments that contain seizures, using both frequency and time domain statistical moment features. The experimental result was obtained from 24 patients in CHB-MIT database. The average accuracy is 94.1, sensitivity is 93.5, specificity is 94.6 and the false positive rate average is 0.054