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

Hilbert transform-based event-related patterns for motor imagery brain computer interface

Breast cancer remains the main cause of cancer deaths among women in the world. As per the statistics, it is the most common killer disease of the new era. Since 2008, breast cancer incidences have increased by more than 20%, while mortality has increased by 14%. The statistics of breast cancer incidences as per GLOBOCAN project for the years 2008 and 2012 show an increase from 22.2 to 27% globally. In India, breast cancer accounts for 25% to 31% of all cancers in women. Mammography and Sonography are the two common imaging techniques used for the diagnosis and detection of breast cancer. Since Mammography fails to spot many cancers in the dense breast tissue of young patients, Sonography is preferred as an adjunct to Mammography to identify, characterize and localize breast lesions. This work aims to improve the performance of breast cancer detection by fusing the texture features from ultrasound elastographic and echographic images through Particle Swarm Optimization. The mean classification accuracy of Optimum Path Forest Classifier is used as an objective function in PSO. Seven performance metrics were computed to study the performance of the proposed technique using GLCM, GLDM, LAWs and LBP texture features through Support Vector Machine classifier. LBP feature provides accuracy, sensitivity, specificity, precision, F1 score, Mathews Correlation Coefficient and Balanced Classification Rate as 96.2%, 94.4%, 97.4%, 96.2%, 95.29%, 0.921, 95.88% respectively. The obtained performance using LBP feature is better compared to the other three features. An improvement of 6.18% in accuracy and 11.19% in specificity were achieved when compared to those obtained with previous works.

Particle swarm optimization based fusion of ultrasound echographic and elastographic texture features for improved breast cancer detection

Study of factors affecting the progression and termination of drug induced Torsade de pointes in two dimensional cardiac tissue

Tissue mimicking phantoms play a crucial role in the development of Acoustic Radiation Force Impulse Imaging. This paper deals with the preparation and study of agar based tissue mimicking phantoms. Acoustic and elastic properties are studied for various concentration of agar samples (1.7%–10% by weight). Acoustic properties are measured using pulse echo transmit receive principle. Young's modulus of the samples are measured using compression test. Reproducible phantom preparation procedures are standardised for matching various properties of normal and abnormal human soft tissues.

Characterization of tissue mimicking phantoms for acoustic radiation force impulse imaging

Dysarthria is a neuromuscular disorder, occurs due to improper coordination of speech musculature. In order to improve the quality of life of people with speech disorder, assistive technology using automatic speech recognition (ASR) systems are gaining importance. Since it is difficult for dysarthric speakers to provide sufficient data, data insufficiency is one of the major problems in building an efficient dysarthric ASR system. In this paper, we focus on handling this issue by pooling data from unimpaired speech database. Then feature space maximum likelihood linear regression (fMLLR) transformation is applied on pooled data and dysarthric data to normalize the effect of inter-speaker variability. The acoustic model built using the combined features (acoustically transformed dysarthric + pooled features) gives an relative improvement of 18.09% and 50.00% over baseline system for Nemours database and Universal Access speech (digit set) database.

M. Ramasubba Reddy

Papers

Hilbert transform-based event-related patterns for motor imagery brain computer interface

Particle swarm optimization based fusion of ultrasound echographic and elastographic texture features for improved breast cancer detection

Study of factors affecting the progression and termination of drug induced Torsade de pointes in two dimensional cardiac tissue

Characterization of tissue mimicking phantoms for acoustic radiation force impulse imaging

Improved acoustic modeling for automatic dysarthric speech recognition