EmotivPRO v3.0
  • EmotivPRO
  • Getting started
    • System requirements
    • Install EMOTIV Launcher
    • Installing EmotivPRO onto your PC or Mac
    • Updating EmotivPRO
    • Logging into EmotivPRO
    • Creating an EmotivID and password
    • Forgotten password
    • Viewing your account information
    • Logging out of EmotivPRO
  • EmotivPRO license options
    • Purchasing an EmotivPRO license
    • Changing the number of seats in your license
    • Organization License
    • Using EmotivPRO offline
    • Cancelling your EmotivPRO license
  • Connecting your headset to EmotivPRO and fitting your headset
    • Connecting your EEG headset to EmotivPRO via Bluetooth
      • Bluetooth connection troubleshooting
    • Pairing your EEG headset with a USB receiver dongle
    • Connecting your EEG headset to EmotivPRO
    • Disconnecting your EEG headset from EmotivPRO
    • Fitting your EEG headset
    • Contact quality map
      • Contact Quality (CQ) vs. EEG Quality (EQ)
  • Using EmotivPRO
    • EmotivPRO home screen and menu
    • EEG quality indicator
    • Battery indicator
    • EPOC+ and EPOC X configurations
  • Data Streams
    • Real time data streams, recording, and playback
    • Raw EEG
    • Frequency Bands
    • Performance Metrics
    • Motion
    • Data Packets
  • Recordings
    • About recordings
    • Starting and stopping a recording
    • Baseline recording
    • Adding a note to a recording
    • Timer
  • Event markers
    • About event markers
    • Baseline markers
    • Serial port markers
    • Keystroke markers
    • USB markers
    • EMOTIV Extender triggering
  • Managing your EEG data recordings
    • Recordings history list
    • Cloud synchronization
    • Playing an EEG data recording
    • Deleting an EEG recording
    • Exporting an EEG data recording
      • EDF files
      • EDF+ / BDF+
      • CSV files
      • JSON files
    • Editing an EEG data recording note
    • Closing a recording
    • Timestamp correction
  • Lab Streaming Layer (LSL)
    • About LSL
    • LSL Outlet
    • LSL Inlet
  • D-Lab Client
    • About D-Lab Client
    • Exporting D-Lab settings from EmotivPRO
    • Starting a Network Data Stream
    • Creating recordings on D-Lab
  • Exported data files
    • Opening a locally saved EDF file
    • Converting EDF files to CSV files
  • Notes on the data
    • DC Offset
    • Code Examples
  • EmotivPro Builder
    • Building experiments with EmotivPRO Builder
  • EmotivPRO Analyzer
    • Analyzing your EEG data with EmotivPRO Analyzer
  • Using Organization Licensing
    • Important notes on Organization Licensing
    • Licensing
    • User roles and permissions
    • Inviting users
    • Accepting an Organization invitation
    • Removing users
    • Organization Data Sharing
  • Release Notes
    • v3.0
    • v3.0.1
    • v3.2
    • v3.2.1
    • v3.3
    • v3.4.1
    • v3.5
    • v3.5.3
    • v3.7.0
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  1. Notes on the data

DC Offset

The measured EEG signal on the headset is converted from an unsigned 14 or 16-bit ADC output to a floating point value which is stored by EmotivPRO. To allow measurement of negative values the (floating) DC level of the signal occurs at approximately 4200 uV. Negative voltages are transmitted less than the average level, and positive voltages are transmitted as greater than the average.

In order to remove the DC offset, especially before performing any kind of analysis such as Fast Fourier Transform (FFT) it is necessary to apply some kind of DC offset removal. The simplest method is to subtract the average from the entire data channel, although this is the least accurate. Ideally you should apply a high-pass filter which matches the characteristics of the electronics - that is, you should use a 0.16Hz first order high-pass filter to remove the background signal (this also removes any longer term drift, which is not achieved by the average subtraction method). Another method is to use an IIR filter to track the background level and subtract it an example is shown below in Matlab pseudocode, assuming the first row has been removed from the array input_data():

IIR_TC = 256;

2 second time constant- adjust as required

EEG_data = input_data( : ,3:16 );

select out only the EEG data

[rows columns]= size(EEG_data);

rows= number of data samples, columns= 14

AC_EEG_data = zeros(rows, columns);

reserve space for the output data file

back= EEG_data( 1, : );

copy the first row of data into the background file

for r = 2 : rows

back= (back* ( IIR_TC- 1 ) + EEG_data( r,:)) I IIR_TC;

AC_EEG_data = EEG_data( r,:)- back;

end

This demonstration code is not efficient in memory and assumes the entire file is available. It is quite straightforward to modify the code to replace the data in the source array rather than making a separate AC-coupled array, and also to run the IIR filter in open-ended form for processing in real time.

Note the vectorised form of the background recalculation at each iteration - each individual channel background average is retained in the relevant column of “back”. At each step the running average is re-estimated using the new input value. Note also that the first IIR _TC samples are biased towards the initial value but this settles down after about 2 * IIR_TC samples.

It is very important to remove the background signal before performing an FFT - you should also apply a tapered window function such as a HANNING transform before executing the FFT to ensure there are no wrapping artefacts where the FFT treats the data as an infinitely repeating sequence, and any mismatch between the first and last samples appears as a STEP FUNCTION in the analysis, injecting noise across the spectrum.

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Last updated 3 years ago