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

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23 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 ...
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2answers
56 views

Why is this MATLAB butterworth filter giving me wrong results

Given a 128 Hz signal, in MATLAB I entered the following command ...
4
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3answers
155 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 ...
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0answers
17 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 , ...
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1answer
26 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 ...
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1answer
63 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
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1answer
54 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
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0answers
36 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
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0answers
17 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 ...
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0answers
22 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
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2answers
90 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. ...
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2answers
48 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
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1answer
85 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, ...
3
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2answers
158 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
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1answer
138 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
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1answer
86 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
125 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
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1answer
107 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
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2answers
128 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
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0answers
91 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
236 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
134 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
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1answer
1k 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
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2answers
328 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
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1answer
264 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
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2answers
154 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 ...
3
votes
1answer
92 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
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1answer
77 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
172 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
2k 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
263 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
210 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 ...