The recording of voltages along the scalp for the purpose of monitoring brain wave activity and localizing sources of ionic currents

learn more… | top users | synonyms

1
vote
3answers
69 views

Pre-processing EEG signals

Good Afternoon Everybody. I am developing filters for EEG signals in these days. During the Develop I find some problems: The filter is FIR (band-pass using a convolution of low-pass filter and ...
0
votes
0answers
21 views

Classifying sleep stages from only EEG

I'm currently working on a project that requires me to classify sleep stages (Awake W, N1, N2, N3 and REM) based on only an EEG. Various algorithms and classifying standards (such as Rechtschaffen ...
0
votes
0answers
53 views

segmenting an EEG data

I have an EEG labeled data, which is the data that used for training, And I want to segment those data based on the time of EEG signal (Time-based Epoching) as a preprocessing step, based on the ...
1
vote
1answer
77 views

FASTICA algoritm produce independent components does not equal to the dimensionality of EEG signals

From my knowledge about independent component analysis the number of channel or variables will not be affected by this algorithm and the number of independent components will be equal to number of ...
0
votes
0answers
12 views

Implication of concatenating EEG data

My question is regarding the mathematical implications of concatenating very small windows from different trials, of the same size and for the same period in time after a stimuli. In other words, ...
0
votes
1answer
57 views

What is bispectrum?

I am working on a project that uses EEG signals of the brain to identify emotional states. While surveying the literature, I came across several references where "derived features of bispectrum" are ...
1
vote
2answers
73 views

What is a 'time-variant' signal?

I have found in the literature a "time-variant signal" term , but I could not understand what that's mean. I know that there is "time-variant systems", but this is the first time, which I see a ...
0
votes
0answers
41 views

Feature extraction and Variance

I see there is a relationship between the Variance of a signal (i.e EEG signal) and features extraction of that signal. for example PCA tend to take almost of the variance of the original signal , but ...
0
votes
2answers
116 views

Why is this MATLAB butterworth filter giving me wrong results

Given a 128 Hz signal, in MATLAB I entered the following command ...
4
votes
3answers
507 views

How does averaging increase the Signal to Noise ratio?

When attempting to spot activities on an EEG waveform given a stimuli, it is recommended that several trials be taken and their average is done to increase the SNR. Intuitively, since noise is not ...
0
votes
0answers
21 views

Common Spatial Patterns importance

The Algorithm Common spatial pattern, is used very frequently in EEG signal processing , and it's based on the idea of maximize variance of one class , and minimize the variance the other class , ...
-1
votes
1answer
43 views

BCI project using EEG signals

I want to make a BCI project, based on EEG signals, to control a robot using the brain's P300 wave. I have read a lot of papers, which give me a wide knowledge on how to deal with those signals, but ...
-1
votes
1answer
81 views

What does “real time” signal processing mean?

I thought this was supposed to be an obvious question, until I finally set up my real time system. So basically I have a transmitter that sends 128 samples/second to a receiver. The transmitted ...
0
votes
1answer
101 views

Why use Wavelet Denosing instead of Band pass Filtering

What is the important advantage of adopting a wavelet based denosing scheme such as SURE, SUREshrink, etc. than using approaches involving low pass, high pass, band pass, band reject filter for signal ...
0
votes
0answers
38 views

Performance of ICA when there is low spatial separation between EEG electrodes

I have EEG data collected from a prototype Low footprint EEG device (2 in x 2 in) placed on the top of the skull. The reference channel is in the center and 8 measurement channels are spread out ...
0
votes
0answers
35 views

Meaning of events in EEGlab

I'm reletively new to the EEG signal processing domain and EEGlab and I'm trying to figure out the meaning and the following terms used in EEGlab and in general context in EEG.It will be helpful if ...
0
votes
0answers
32 views

What are some interesting things I can do with EEG data?

I have an EEG device and would like to experiment with it a bit. Some of the data I can read from the device, in real time, are: ...
0
votes
2answers
188 views

direct Frequency domain FIR filtering vs Overlap-add method

I'm trying to do bandpass filtering of a EEG signal samples at 250Hz and benchmarking the following 4 methods of FIR filtering for different filter orders. The length of the signal is 15000 samples. ...
-3
votes
2answers
65 views

What is the best OS for real time signal processing? [closed]

I'm implementing a BCI-Wheel Chair Control System where signal are extracted from the arm into MATLAB -> feature extraction -> classification -> Control signal The delay should be minimal between ...
1
vote
1answer
89 views

Epoched data to a continuous signal

if we create a continuous signal from epoched data (say, EEG data with 100 epoches), what kind of problems it may cause( i am only aware of edge artifacts ) and how i can avoid them in analysis (say, ...
4
votes
3answers
268 views

How do you apply Kalman Filter to track a signal?

The example that I've seen on state estimation involves deriving the ABCD matrix of a physical system (i.e. falling object) and tracking that object. I would like to use Kalman Filter for signal ...
1
vote
1answer
219 views

filtfilt giving unexpected results

I am trying to filter EEG signals using butterworth filter and filtfilt. I have gone through a lot of documentation and these 2 commands seem sufficient for filtering. However, the results are ...
1
vote
1answer
161 views

Filtering the EEG signals

I have very limited knowledge of DSP, so I apologize if my question is trivial :) I have an EEG signal from which I need to extract different frequency bands. For example, waves in the the frequency ...
0
votes
1answer
214 views

Simple FFT filtering vs. e.g. butterworth filtering

I am currently working on functional connectivity analysis of EEG, and need to bandpass filter my data into different frequency bands (Delta, Theta, Alpha and Beta). An important thing is that the ...
0
votes
1answer
196 views

EEG signal processing

I have an EEG data set downloaded from physionet. I want to play that data into a brain monitoring device like for example BIS monitor or Narcotrend or any other similar brain monitoring device.I use ...
1
vote
2answers
234 views

Using ICA on EEG signals for feature extraction

I am attempting to use ICA (FastICA via scikit-learn) on EEG signals from seven electrodes per subject for feature extraction and identity classification – that is to extract signal which is related ...
1
vote
0answers
151 views

Common mode rejection (in software) without a reference channel? EEG data

Summary I need to remove artifact that appears strongly on all channels in my EEG data. It's already recorded (from another lab) so I can't use hardware solutions. Also: band overlap and no reference ...
2
votes
1answer
418 views

Large Laplacian Spatial Filter on EEG?

I want to know that why large laplacian spatial filtering is done on EEG signals? I tried a lot but could not find material on LLSF. And what is meant by spatial filter ( in context of Digital Signal ...
1
vote
1answer
279 views

Denoise EEG signal by using Daubechies function

I have an EEG signal and it contains eye blink artifacts. I read some references and know that it is possible to detect eye blinks and remove them by using wavelet transforms, but I don't know that ...
1
vote
1answer
2k views

Wavelet Transfrom + Power Spectral Density (using Matlab)

I don't have background knowledge about signal processing before and new at Matlab too. I have EEG data (with noise removed) 1x128; sampling rate = 128 Hz, It's means that I have 1 sec. data right? ...
0
votes
2answers
444 views

Are there any good texts on EEG analysis geared towards programmers

I just have a quick question. I'm a second year computer science major with a background in C# and C++. I'm interested in studying neuroscience after graduation, and I've been researching some ...
1
vote
1answer
371 views

What is the standard way to use z-score with EEG?

I can't understand how to use normalization technique for EEG. Is it used for results of FFT or for raw EEG signal? Are there different methods with common name "z-transform"? And what z-score used in ...
5
votes
2answers
174 views

Correct method for drawing waveforms

I need to draw waveforms for biometric data like ECG and EEG signals. When I have more samples than pixels at the X-axis, I need to draw a vertical line between the MIN and MAX sample-value for that ...
5
votes
1answer
145 views

Assumptions for Hurst exponent calculation

Are there any general assumptions for the calculation of the Hurst exponent? Does the signal need to be stationary, for example? Does it depend on the method? What about the length of the time ...
1
vote
1answer
93 views

What features to use for multilayer perceptron model of an EEG signal?

I have an EEG signal that I want to extract features to apply multilayer perceptron (MLP). What I should use, Fourier or wavelet coefficients?
6
votes
1answer
188 views

What is averaging and how can it be done?

I'm studying about the analysis of (mainly) fMRI and EEG data. Multiple times it's mentioned that to reduce noise, you can use averaging, but nothing more detailed than that. There never is literally ...
2
votes
2answers
3k views

Howto calculate SNR for EEG data?

This question is about SNR in the context of EEG. (a related question about SNR) I am interested in calculating The SNR of an ERP. My motivation is: To calculate the "signal-to-noise ratio" and from ...
7
votes
2answers
283 views

How can I distinguish between two similar EEG signals?

I have two EEG signals that are very similar. The difference is only in amplitudes. However, they are coming from two different cognitive processes. What are some methods, beside FFT, for ...
12
votes
3answers
224 views

Is ICA appropriate for separating mixed signals when all source signals are NOT detectable at all sensors?

A generic implementation of ICA for the separation of a mixture of $N$ signals into their $M$ constituent components requires that the signals be assumed to be a linear instantaneous mixture of the ...