Extraction of sparse spatial filters using Oscillating Search

Common Spatial Pattern algorithm (CSP) is widely used in Brain Machine Interface (BMI) technology to extract features from dense electrode recordings by using their weighted linear combination. However, the CSP algorithm, is sensitive to variations in channel placement and can easily overfit to the data when the number of training trials is insufficient. Construction of sparse spatial projections where a small subset of channels is used in feature extraction, can increase the stability and generalization capability of the CSP method. The existing 0 norm based sub-optimal greedy channel reduction methods are either too complex such as Backward Elimination (BE) which provided best classification accuracies or have lower accuracy rates such as Recursive Weight Elimination (RWE) and Forward Selection (FS) with reduced complexity. In this paper, we apply the Oscillating Search (OS) method which fuses all these greedy search techniques to sparsify the CSP filters. We applied this new technique on EEG dataset IVa of BCI competition III. Our results indicate that the OS method provides the lowest classification error rates with low cardinality levels where the complexity of the OS is around 20 times lower than the BE. © 2012 IEEE

Classification of single trial motor imagery EEG recordings with subject adapted non-dyadic arbitrary time-frequency tilings

PubMedID: 16921207We describe a new technique for the classification of motor imagery electroencephalogram (EEG) recordings in a brain computer interface (BCI) task. The technique is based on an adaptive time-frequency analysis of EEG signals computed using local discriminant bases (LDB) derived from local cosine packets (LCP). In an offline step, the EEG data obtained from the C3/C 4 electrode locations of the standard 10/20 system is adaptively segmented in time, over a non-dyadic grid by maximizing the probabilistic distances between expansion coefficients corresponding to left and right hand movement imagery. This is followed by a frequency domain clustering procedure in each adapted time segment to maximize the discrimination power of the resulting time-frequency features. Then, the most discriminant features from the resulting arbitrarily segmented time-frequency plane are sorted. A principal component analysis (PCA) step is applied to reduce the dimensionality of the feature space. This reduced feature set is finally fed to a linear discriminant for classification. The online step simply computes the reduced dimensionality features determined by the offline step and feeds them to the linear discriminant. We provide experimental data to show that the method can adapt to physio-anatomical differences, subject-specific and hemisphere-specific motor imagery patterns. The algorithm was applied to all nine subjects of the BCI Competition 2002. The classification performance of the proposed algorithm varied between 70% and 92.6% across subjects using just two electrodes. The average classification accuracy was 80.6%. For comparison, we also implemented an adaptive autoregressive model based classification procedure that achieved an average error rate of 76.3% on the same subjects, and higher error rates than the proposed approach on each individual subject. © 2006 IOP Publishing Ltd

Prediction of pharmacologically induced baroreflex sensitivity from local time and frequency domain indices of R-R interval and systolic blood pressure signals obtained during deep breathing

PubMedID: 21550604Pharmacological measurement of baroreflex sensitivity (BRS) is widely accepted and used in clinical practice. Following the introduction of pharmacologically induced BRS (p-BRS), alternative assessment methods eliminating the use of drugs were in the center of interest of the cardiovascular research community. In this study we investigated whether p-BRS using phenylephrine injection can be predicted from non-pharmacological time and frequency domain indices computed from electrocardiogram (ECG) and blood pressure (BP) data acquired during deep breathing. In this scheme, ECG and BP data were recorded from 16 subjects in a two-phase experiment. In the first phase the subjects performed irregular deep breaths and in the second phase the subjects received phenylephrine injection. From the first phase of the experiment, a large pool of predictors describing the local characteristic of beat-to-beat interval tachogram (RR) and systolic blood pressure (SBP) were extracted in time and frequency domains. A subset of these indices was selected using twelve subjects with an exhaustive search fused with a leave one subject out cross validation procedure. The selected indices were used to predict the p-BRS on the remaining four test subjects. A multivariate regression was used in all prediction steps. The algorithm achieved best prediction accuracy with only two features extracted from the deep breathing data, one from the frequency and the other from the time domain. The normalized L2-norm error was computed as 22.9% and the correlation coefficient was 0.97 (p=0.03). These results suggest that the p-BRS can be estimated from non-pharmacological indices computed from ECG and invasive BP data related to deep breathing. © 2011 Elsevier Ltd.This study was supported by The Scientific and Technological Research Council of Turkey (TÜBİTAK)

Prediction of pharmacologically induced baroreflex sensitivity from noninvasive baroreflex, heart rate and systolic blood pressure variability indices

IEEE 17th Signal Processing and Communications Applications Conference -- APR 09-11, 2009 -- Antalya, TURKEYWOS: 000273935600046Assessment of baroreflex sensitivity (BRS) using vasoactive drugs is widely accepted in clinical research. It has been reported that decrease in BRS is an indicative of increased risk of sudden cardiac death. Due to its clinical importance, alternative baroreflex assessment methods have been developed which eliminate use of drugs and since then they are called non-invasive methods. In this study BRS index obtained by drug based assessment was predicted from subset of noninvasive BRS indices extracted from heart rate and systolic pressure time series. A leave one out method was employed to search subset of indices which gives the highest correlation with invasive and predicted BRS. Two predictors provided the highest correlation (0.87). The algorithm selected consistently normalized cross-power in Mayer frequency band and average magnitude square coherence in high frequency band as predictors for all 16 subjects.IEE

Evoked potentials

The quest toward a specific biomarker for migraine stands among the biggest challenges of the last 50 years. Electrophysiological techniques are particularly suitable to study the nervous system in human beings. They are noninvasive, riskless and quite easy to perform and have a temporal resolution that cannot be achieved with other methods. Among them, the visual-evoked modality is being widely studied for several decades. Higher amplitude of fundamental harmonic from steady-state visual stimulation is commonly found in episodic migraine. Many studies performed interictally in groups of episodic patients have shown a habituation deficit of visual evoked potentials, even if this finding has been a matter of controversy. An abnormal thalamic control of information reaching the cortex, which in turn causes an altered degree of lateral inhibition of the visual cortex, could be the key of this functional abnormality, which normalizes during or close to a migraine attack. Along the same line, a habituation deficit has been demonstrated using a somatosensory modality (SSEPs), the magnitude of the habituation deficit being significantly correlated to the evolution of migraine. Additional works highlighted a less-efficient subcortical inhibition of sensory cortices. As far as the auditory modality is concerned, a stronger stimulus intensity dependence of late, long-latency, auditory evoked cortical potentials (IDAP) was found between attacks in migraineurs compared with controls. It seems also worthwhile to notice that an interhemispheric asymmetry of responses has been described using most sensory stimulations. Using single-pulse transcranial magnetic stimulation (sTMS) over the visual cortex, a higher phosphene prevalence and a lower threshold were found in migraine with aura patients. Otherwise, resting-state motor or phosphene thresholds obtained with sTMS in episodic patients provided discrepant results. In chronic migraine (CM), neurophysiologic signs of sensitization have been reported while recording SSEPs. Interestingly, a simultaneous analysis of SSEP habituation and thalamocortical loop activation in chronic subjects showed a neurophysiological pattern similar to that of ictal episodic migraine. In medication overuse headache patients, SSEPs suggested a persistent cortical sensitization. The recorded habituation abnormalities appear to vary according to the overused drug. Akin to results of SSEP studies, VEP amplitudes habituate normally during stimulus repetition in CM and may change with the transition from CM to episodic migraine, switching from normal to deficient habituation. In conclusion, studies of evoked potentials in migraine show that the migraine brain processes sensory information differently from healthy subjects. The most frequently detected peculiarity during the migraine pain-free phase is an excessive cortical responsivity to almost any type of sensory stimulation. The cortical hyperresponsivity is not constant in migraine patients and may not be reproducible. The reasons for these between-studies discrepancies are multifaceted, and they reflect the complex pathophysiology of the disease