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

Medical studies have shown that EEG of Alzheimer's disease (AD) patients is “slower” (i.e., contains more low-frequency power) and is less complex compared to age-matched healthy subjects. The relation between those two phenomena has not yet been studied, and they are often silently assumed to be independent. In this paper, it is shown that both phenomena are strongly related. Strong correlation between slowing and loss of complexity is observed in two independent EEG datasets: (1) EEG of predementia patients (a.k.a. Mild Cognitive Impairment; MCI) and control subjects; (2) EEG of mild AD patients and control subjects. The two data sets are from different patients, different hospitals and obtained through different recording systems. The paper also investigates the potential of EEG slowing and loss of EEG complexity as indicators of AD onset. In particular, relative power and complexity measures are used as features to classify the MCI and MiAD patients versus age-matched control subjects. When combined with two synchrony measures (Granger causality and stochastic event synchrony), classification rates of 83% (MCI) and 98% (MiAD) are obtained. By including the compression ratios as features, slightly better classification rates are obtained than with relative power and synchrony measures alone.

/pdf/slowing-and-loss-of-complexity-in-alzheimer-s-eeg-two-sides-2d2m3zi8x1.pdf

Slowing and Loss of Complexity in Alzheimer's EEG: Two Sides of the Same Coin?

The objective is to increase the number of selections in brain computer interfaces (BCI) by recording and analyzing the steady state visual evoked potential response to dual stimulation. A BCI translates the VEP signals into user commands. The frequency band from which stimulation frequency can be selected is limited for SSVEP. This paper discusses a method to increase the number of commands by using a suitable combination of frequencies for stimulation. A biopotential amplifier based on the driven right leg circuit (DRL) is used to record 60 s epochs of the SSVEP (O(z)-A(1)) on 15 subjects using simultaneous overlapped stimulation (6, 7, 12, 13 and 14 Hzs and corresponding half frequencies). The power spectrum of each recording is obtained by frequency domain averaging of 400 ms SSVEPs and the spectral peaks were normalized. The spectral peaks of the combination frequencies of stimulation are predominant compared to individual stimulating frequencies. This method increases the number of selections by using a limited number of stimulating frequencies in BCI. For example, six selections are possible by generating only three frequencies.

http://iopscience.iop.org/article/10.1088/0967-3334/27/1/006/pdf

A novel multiple frequency stimulation method for steady state VEP based brain computer interfaces

http://www.dauwels.com/Papers/BSPC%202011.pdf

A Two-Dimensional approach for lossless EEG compression

An efficient preprocessing technique of arranging an electroencephalogram (EEG) signal in matrix form is proposed for real-time lossless EEG compression. The compression algorithm consists of an integer lifting wavelet transform as the decorrelator with set partitioning in hierarchical trees as the source coder. Experimental results show that the preprocessed EEG signal gave improved rate-distortion performance, especially at low bit rates, and less encoding delay compared to the conventional one-dimensional compression scheme.

Efficient preprocessing technique for real-time lossless EEG compression

An adequate amount of blood supply is necessary for the proper functioning of all body organs as blood carries all the nutrients and oxygen that our body needs to stay healthy. Various diseases cause an impaired supply of blood to the organs. The measurement of the blood flow can therefore provide essential information for the diagnosis of diseases. Since changes in blood flow occurs with the very initial stage of disease, with a fast, reliable and noninvasive blood flow measurement technique, the physicians would be provided with new options for early disease diagnosis. Beginning with brief overview of early methods of blood flow measurement, this article reviews the recent blood flow measuring techniques in detail and ends with suggestions for future research in related areas.

/pdf/measuring-blood-flow-techniques-and-applications-a-review-36u1niag5n.pdf

M. Ramasubba Reddy

Papers

Slowing and Loss of Complexity in Alzheimer's EEG: Two Sides of the Same Coin?

A novel multiple frequency stimulation method for steady state VEP based brain computer interfaces

A Two-Dimensional approach for lossless EEG compression

Efficient preprocessing technique for real-time lossless EEG compression

Measuring blood flow: techniques and applications - a review