Probability Distributions.- Linear Models for Regression.- Linear Models for Classification.- Neural Networks.- Kernel Methods.- Sparse Kernel Machines.- Graphical Models.- Mixture Models and EM.- Approximate Inference.- Sampling Methods.- Continuous Latent Variables.- Sequential Data.- Combining Models.

Pattern Recognition and Machine Learning

Steady-state visually evoked potentials: focus on essential paradigms and future perspectives.

Brain-computer interface (BCI) systems based on the steady-state visual evoked potential (SSVEP) provide higher information throughput and require shorter training than BCI systems using other brain signals. To elicit an SSVEP, a repetitive visual stimulus (RVS) has to be presented to the user. The RVS can be rendered on a computer screen by alternating graphical patterns, or with external light sources able to emit modulated light. The properties of an RVS (e.g., frequency, color) depend on the rendering device and influence the SSVEP characteristics. This affects the BCI information throughput and the levels of user safety and comfort. Literature on SSVEP-based BCIs does not generally provide reasons for the selection of the used rendering devices or RVS properties. In this paper, we review the literature on SSVEP-based BCIs and comprehensively report on the different RVS choices in terms of rendering devices, properties, and their potential influence on BCI performance, user safety and comfort.

/pdf/a-survey-of-stimulation-methods-used-in-ssvep-based-bcis-38omyw8mqs.pdf

A survey of stimulation methods used in SSVEP-based BCIs

In this paper, novel methods for detecting steady-state visual evoked potentials using multiple electroencephalogram (EEG) signals are presented. The methods are tailored for brain-computer interfacing, where fast and accurate detection is of vital importance for achieving high information transfer rates. High detection accuracy using short time segments is obtained by finding combinations of electrode signals that cancel strong interference signals in the EEG data. Data from a test group consisting of 10 subjects are used to evaluate the new methods and to compare them to standard techniques. Using 1-s signal segments, six different visual stimulation frequencies could be discriminated with an average classification accuracy of 84%. An additional advantage of the presented methodology is that it is fully online, i.e., no calibration data for noise estimation, feature extraction, or electrode selection is needed

Multiple Channel Detection of Steady-State Visual Evoked Potentials for Brain-Computer Interfaces

A textbook of physiology

The aim of this paper is to classify both (left and right) hand motor imagery (MI) tasks using event-related (de) synchronization (ERD/ERS) patterns based on the inter trial variance (IV). In this work, publicly available online BCI-2003 competition data set was used which contains MI based Electroencephalography (EEG) data of a single subject. The raw MI based EEG signals were extracted from C3 and C4 channels and band pass filtered to mu (8–12 Hz) frequency band. The mean, inter trial variance and ERD/ERS patterns were calculated in both the channels for both MI tasks of the subject. The patterns were used as feature vector and input to the different machine learning classifiers. The logistic regression and Naive Bayesian classifiers were used for the classification of both MI tasks of the subject. For logistic regression classifier, the accuracy, precision and sensitivity were found to be 92.40%, 92.00% and 92.00%, respectively. Meanwhile, 92.19% accuracy, 92.00% precision and 92.00% sensitivity were obtained for the Naive Bayesian classifier. The logistic regression classifier provides maximum classification accuracy (92.40%) in comparison with the existing methods (87.90–90.20%). Result shows that, the proposed method can quantify ERD/ERS patterns effectively in both the channels and also classifies both MI tasks of the subject successfully.

Classification of Motor Imagery Tasks Using Inter Trial Variance In The Brain Computer Interface

Fusion of MLO and CC View Binary Patterns to Improve the Performance of Breast Cancer Diagnosis

In this study, solid, stable, and cost-effective optical phantoms of scalp–skull, white matter and grey matter are developed by inverse method. To begin with, to obtain a range of optical parameters, absorption and reduced scattering coefficients (μa and 
$$ {\mu }\ifmmode{'}\else$'$\fi_{{\text{s}}} , $$
 respectively), 20 homogeneous phantoms were made of paraffin wax by using optically contrast black and highly scattering white colouring materials in different combination. By comparing the measured reflectance values for each phantom got from the four channel reflectometer with that obtained from steady-state diffusion equation, the values of μa and 
$$ {\mu }\ifmmode{'}\else$'$\fi_{{\text{s}}} $$
 were determined. Next, phantoms which exhibit specific optical properties of scalp–skull, white and grey matter are developed iteratively by comparing actual reflectance measurements got by adjusting the colour concentration with the predicted reflectance values from the diffusion equation. This is done as follows: to obtain μa of 0.04 mm−1 for scalp–skull, 9.5 mg of black dye per 100 ml of wax added since more attenuation of light occurs in bone tissue. To obtain a 
$$ {\mu }\ifmmode{'}\else$'$\fi_{{\text{s}}} $$
 6.0 mm−1 for white matter in brain tissue, 190 mg of white dye per 100 ml of wax was used to facilitate more scatter of light. The colour concentrations of phantoms were then adjusted to obtain the predetermined values of optical parameters for scalp–skull, grey and white matter.

Brain tissue phantoms for optical near infrared imaging

Dysarthria refers to a group of motor speech disorders as the result of any neurological injury to the speech production system. Dysarthric speech is characterised by poor speech articulation, resulting in degradation in speech quality. Hence, it is important to correct or improve dysarthric speech so as to enable people having dysarthria to communicate better. The aim of this paper is to improve the quality of continuous speech of several people suffering from dysarthria. Experiments in the current work use two databasesNemours database and speech data collected from a dysarthric speaker of Indian origin. Durational analysis of dysarthric speech versus normal speech is performed. Based on the analysis, manual modifications are made directly to the speech waveforms and an automatic technique is developed for the same. Evaluation tests indicate an average preference of 78.44% and 67.04% for the manually and automatically altered speech over the original dysarthric speech, thus emphasising the effect of durational modifications on the perception of speech quality. Intelligibility of speech generated by three techniques, namely, proposed automatic modification technique, a formant re-synthesis technique, and an HMMbased adaptive system, is compared.

Improvement of Continuous Dysarthric Speech Quality

Microcirculation is essential for the proper supply of oxygen and nutritive substances to the biological tissue and the removal of waste products of metabolism. The determination of blood flow in the capillaries is therefore of great interest to clinicians. A comparison has been carried out using the developed non-invasive, non-contact and whole field laser speckle contrast imaging (LSCI) based technique and as well as a commercially available laser Doppler blood flowmeter (LDF) to evaluate blood flow at the finger tip and elbow and is presented here. The LSCI technique gives more quantitative information on the velocity of blood when compared to the perfusion values obtained using the LDF. Measurement of blood flow in capillaries can be of great interest to clinicians in the diagnosis of vascular diseases of the upper extremities. Keywords—Blood flow, Laser Doppler flowmeter, LSCI, speckle

/pdf/non-invasive-capillary-blood-flow-measurement-laser-speckle-2x53yp3wf0.pdf

M. Ramasubba Reddy

Papers

Classification of Motor Imagery Tasks Using Inter Trial Variance In The Brain Computer Interface

Fusion of MLO and CC View Binary Patterns to Improve the Performance of Breast Cancer Diagnosis

Brain tissue phantoms for optical near infrared imaging

Improvement of Continuous Dysarthric Speech Quality

Non-Invasive Capillary Blood Flow Measurement: Laser Speckle and Laser Doppler