High Accuracy Decoding of Movement Target Direction in Non-Human Primates Based on Common Spatial Patterns of Local Field Potentials

BACKGROUND: The current development of brain-machine interface technology is limited, among other factors, by concerns about the long-term stability of single- and multi-unit neural signals. In addition, the understanding of the relation between potentially more stable neural signals, such as local field potentials, and motor behavior is still in its early stages. METHODOLOGY/PRINCIPAL FINDINGS: We tested the hypothesis that spatial correlation patterns of neural data can be used to decode movement target direction. In particular, we examined local field potentials (LFP), which are thought to be more stable over time than single unit activity (SUA). Using LFP recordings from chronically implanted electrodes in the dorsal premotor and primary motor cortex of non-human primates trained to make arm movements in different directions, we made the following observations: (i) it is possible to decode movement target direction with high fidelity from the spatial correlation patterns of neural activity in both primary motor (M1) and dorsal premotor cortex (PMd); (ii) the decoding accuracy of LFP was similar to the decoding accuracy obtained with the set of SUA recorded simultaneously; (iii) directional information varied with the LFP frequency sub-band, being greater in low (0.3-4 Hz) and high (48-200 Hz) frequency bands than in intermediate bands; (iv) the amount of directional information was similar in M1 and PMd; (v) reliable decoding was achieved well in advance of movement onset; and (vi) LFP were relatively stable over a period of one week. CONCLUSIONS/SIGNIFICANCE: The results demonstrate that the spatial correlation patterns of LFP signals can be used to decode movement target direction. This finding suggests that parameters of movement, such as target direction, have a stable spatial distribution within primary motor and dorsal premotor cortex, which may be used for brain-machine interfaces

An orthogonal scaling and wavelet function pair for analysis and synthesis of electroencephalography signals

IEEE 15th Signal Processing and Communications Applications Conference -- JUN 11-13, 2007 -- Eskisehir, TURKEYWOS: 000252924600293An orthogonal scaling and wavelet function pair is generated based on excitatory postsynaptic potential. Since scaling function is similar to a signal obtained as a sum of action potentions assuming that their occurance is Gausian they can be used in analysis and synthesis of an electroencephalography signals. The orthogonal scaling and wavelet functions which have been created can be an alternative to scaling and wavelet functions found in the literature.IEE

Detection of Exudates from Digital Fundus Images of Diabetic Retinopathy Patients

21st National Biomedical Engineering Meeting (BIYOMUT) -- NOV 24-DEC 26, 2017 -- Istanbul, TURKEYWOS: 000447671500033Diabetes is a condition where the body does not produce enough insulin to convert sugar to energy, leading to a build up of sugar in the blood. This leads to a number of problems, including diabetic retinopathy. Diabetic retinopathy is a complication of diabetes that causes damage to the blood vessels of the retina that allowing you to see fine detail. It causes progressive damage to the retina. This paper proposes a simple yet an efficient approach for automatic detection of the exudates of the Diabetic Retinopathy. The detection of exudates of diabetic retinopathy is composed of eight steps: 1. RGB to gray conversion. 2. Moving mean to 0.5 3. Max Filter 4. Threshold specification 5. Optic disk removal 6. Remove of objects which is not exudates 7. Thresholding original image using the specified exudate regions 8. Computing statistical measures

ELECTROMYOGRAPHIC CHARACTERISTICS OF THE QUADRICEPS FEMORIS DURING PERFORMANCE OF THE WINGATE TEST

WOS: 000409261000004The purpose of this study was to investigate the relationship between the Wingate Anaerobic Test (WAT) outputs and the Electromyography (EMG) parameters. Seventeen sedentary college males participated in the study (mean +/- SD, age 20.5 +/- 2.4 y; height 174.2 +/- 4.3 cm; body mass 66.2 +/- 7.6 kg). Surface electromyographic signals of vastus medialis, vastus lateralis and rektus femoris were recorded during WAT. Power, normalized power, cadence, Mean power frequency (MPF) of each muscle and Root Mean Square (RMS) EMG were calculated as 5 s averages. Mean differences in power and cadence, mean EMG frequency and RMS EMG were analyzed by repeated measures one way ANOVA with Bonferroni post hoc adjustment for multiple pairwise comparisons. Pearson's correlation coefficient was used to evaluate the relationship between the WAT performance variables and muscle EMG outputs All data are presented as mean +/- SD. The peak power and cadence decreased significantly (p0.01). There was a correlation between peak power, normalized power, cadence and MPF of the quadriceps muscles also. The results suggest that there was a correlation between mean power frequencies of vastus medialis, vastus lateralis, rectus femoris and WAT performance. The decreases of the peak power and cadence should be related to the decreases of the mean frequencies of the quadriceps muscles

Evoked Potential in Response to Familiar and Unfamiliar Face Stimuli and Their Time-Location Analysis

19th National Biomedical Engineering Meeting (BIYOMUT) -- NOV 05-06, 2015 -- Istanbul, TURKEYWOS: 000380507300025Evoked potentials (EP) occur against a specific stimulus as a response in EEG signal. The objective of this study is to determine time and location where the evoked potentials obtained in a familiar/unfamiliar face recognition experiments, are most distant. EEG signals have been acquired from 10 subjects in different sessions and the time and position where ERP signals obtained in response to familiar and unfamiliar face stimuli have been analyzed using statistical methods. This study can be considered as a first step for an EEG signal classification problem of an familiar and unfamiliar face recognition experiment. Such a classification both can help to the experts in the diagnosis of some diseases in the visual memory areas of the brain and can contribute to the identification of suspects in criminal inspections

Signal processing and modelling of the response of crayfish photosensitive ganglion

Proceedings of the 1st 1995 Regional Conference IEEE Engineering in Medicine & Biology Society and 14th Conference of the Biomedical Engineering Society of India --15 February 1995 through 18 February 1995 -- New Delhi, India --The terminal abdominal ganglion of the crayfish acts as a photoreceptor. When this sensory receptor is illuminated, the crayfish will walk randomly into a region of subdued illumination. In the 1950's, Lawrence and his group has recorded the response of the crayfish for a step change in light intensity with intra- and extra-cellular electrodes. As it is difficult to produce an impulse of light, they recorded the response with a step change in light. The mid-range pulses, the pulses of interest, were shaped to produce constant-size pulses, then integrated to produce a voltage whose level corresponds to the instantaneous output frequency of the neuron population. In this paper, an attempt is made to process the recorded data which contains signal corrupted with noise. The data is first digitized and then processed by Fourier transform in order to obtain relative frequency of the signal and noise. This has revealed that the signal frequency is below 3 Hz and the noise frequency is above this frequency. Therefore the noisy data is processed through a low-pass filter and the noise-free signal is obtained. A model to fit the noise-free response is constructed. The response of the model is compared with that of the noise-free signal. It is found that they are in good agreement

Malondialdehyde Level in the Cord Blood of Newborn Infants

Objective: In this study, we aim to demonstrate that measurement of the malondialdehyde (MDA) level in the umbilical cord blood of newborn infants born via cesarean section (C/S) and normal vaginal delivery (NVD) is indicative of oxidative stress during the perinatal period. Methods: The study was conducted at Bakirkoy Training and Research Hospital between January 2006 and April 2006 on 15 newborns born via elective C/S, 15 newborns born via emergency C/S, and 15 newborns born via normal vaginal delivery. Complete blood count, total bilirubin, glucose, creatinine phosphokinase (CPK), uric acid, iron, blood gas, and malondialdehyde levels were measured in the umbilical cord blood Findings: Malondialdehyde levels in the umbilical cord blood in the emergency C/S and NVD groups were found to be statistically and significantly higher than those in the elective C/S group. In the emergency C/S group, it was determined that the malondialdehyde level increased as the oxygen saturation of the umbilical cord blood increased. In the NVD group, a positive correlation was detected between the total bilirubin and malondialdehyde levels in the umbilical cord blood. In the emergency C/S group, the malondialdehyde level was recorded to be high in the infants with high level of uric acid in the umbilical cord blood. Conclusion: We concluded that the malondialdehyde level in umbilical cord blood could serve as an indication of perinatal oxidative stress and that it could thus help in preventing permanent damage

Malondialdehyde Level in the Cord Blood of Newborn Infants

Objective: In this study, we aim to demonstrate that measurement of the malondialdehyde (MDA) level in the umbilical cord blood of newborn infants born via cesarean section (C/S) and normal vaginal delivery (NVD) is indicative of oxidative stress during the perinatal period

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