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

Classification of motor imagery EEG recordings with subject specific time-frequency patterns

IEEE 14th Signal Processing and Communications Applications -- APR 16-19, 2006 -- Antalya, TURKEYWOS: 000245347800137We introduce an adaptive time-frequency plane feature extraction and classification system for the classification of motor imagery EEG recordings in a Brain Computer Interface task. First the EEG is segmented in time axis with a merge/divide strategy. This is followed by a clustering procedure in the frequency domain in each selected time segment to choose the most discriminant frequency features. The resulting adaptively selected time-frequency features are processed by principal component analysis - PCA for dimension reduction and fed to a linear discriminant classifier. The algorithm was applied to all nine subjects of the 2002 BCI Competition. The classification performance of our proposed algorithm varied between 70% and 92.6% for each subject, which gives an average classification accuracy of 80.6%. The algorithm outperformed the reference standard Adaptive Autoregressive model based classification procedure for all subjects. This latter approach had an average error rate of %76.3 on the same subjects. We observed that the time-frequency tiling selected by the algorithm for EEG signal classification differs from subject to subject. Furthermore, the two hemispheres of the same subject are represented by distinct time-frequency segmentations and features. We argue that the method can adapt automatically to physio-anatomical differences and subject specific motor imagery patterns.IEE

Detection of early morning daily activities with static home and wearable wireless sensors

This paper describes the infrastructure of a flexible, cost-effective, wireless in-home activity monitoring system for assisting patients with cognitive impairments due to traumatic brain injury (TBI) and presents initial applications in detecting functional activities executed in the early morning. The system issues automatic reminders or guidance only when needed and as non-intrusive as possible by continuously monitoring of the activities of the TBI patient. The system integrates static home and wearable wireless sensors to detect the functional activity of the patient. It locates the subject with fixed home sensors, and captures the details of the executed activity with a wearable wireless accelerometer. We show promising experimental results from 7 subjects while completing washing face, shaving and brushing activities. The proposed system achieved 93.5%, 92.5 % and 95.6 % classification accuracy in the recognition of these three tasks respectively

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

Classification of Hazelnut Kernels by Using Impact Acoustic Time-Frequency Patterns

Hazelnuts with damaged or cracked shells are more prone to infection with aflatoxin producing molds (Aspergillus flavus). These molds can cause cancer. In this study, we introduce a new approach that separates damaged/cracked hazelnut kernels from good ones by using time-frequency features obtained from impact acoustic signals. The proposed technique requires no prior knowledge of the relevant time and frequency locations. In an offline step, the algorithm adaptively segments impact signals from a training data set in time using local cosine packet analysis and a Kullback-Leibler criterion to assess the discrimination power of different segmentations. In each resulting time segment, the signal is further decomposed into subbands using an undecimated wavelet transform. The most discriminative subbands are selected according to the Euclidean distance between the cumulative probability distributions of the corresponding subband coefficients. The most discriminative subbands are fed into a linear discriminant analysis classifier. In the online classification step, the algorithm simply computes the learned features from the observed signal and feeds them to the linear discriminant analysis (LDA) classifier. The algorithm achieved a throughput rate of 45 nuts/s and a classification accuracy of 96% with the 30 most discriminative features, a higher rate than those provided with prior methods

Prediction of STN-DBS Electrode Implantation Track in Parkinson’s Disease by Using Local Field Potentials

Optimal electrophysiological placement of the DBS electrode may lead to better long term clinical outcomes. Inter-subject anatomical variability and limitations in stereotaxic neuroimaging increase the complexity of physiological mapping performed in the operating room. Microelectrode single unit neuronal recording remains the most common intraoperative mapping technique, but requires significant expertise and is fraught by potential technical difficulties including robust measurement of the signal. In contrast, local field potentials (LFPs), owing to their oscillatory and robust nature and being more correlated with the disease symptoms, can overcome these technical issues. Therefore, we hypothesized that multiple spectral features extracted from microelectrode-recorded LFPs could be used to automate the identification of the optimal track and the STN localization. In this regard, we recorded LFPs from microelectrodes in three tracks from 22 patients during DBS electrode implantation surgery at different depths and aimed to predict the track selected by the neurosurgeon based on the interpretation of single unit recordings. A least mean square (LMS) algorithm was used to de-correlate LFPs in each track, in order to remove common activity between channels and increase their spatial specificity. Subband power in the beta band (11-32Hz) and high frequency range (200-450Hz) were extracted from the de-correlated LFP data and used as features. A linear discriminant analysis (LDA) method was applied both for the localization of the dorsal border of STN and the prediction of the optimal track. By fusing the information from these low and high frequency bands, the dorsal border of STN was localized with a root mean square error of 1.22 mm. The prediction accuracy for the optimal track was 80%. Individual beta band (11-32Hz) and the range of high frequency oscillations (200-450Hz) provided prediction accuracies of 72% and 68% respectively. The best prediction result obtained with monopolar LFP data was 68%. These results establish the initial evidence that LFPs can be strategically fused with computational intelligence in the operating room for STN localization and the selection of the track for chronic DBS electrode implantation