Signal amplification, filtering and pre-processing

EEG and EMG signals recorded from the body surface have very small voltages and this requires considerable amplification to produce a signal large enough for digitising. The output from most amplifiers is analogue (a continuous-valued function). Analogue- to-digital (A/D) conversion essentially converts this continuous-valued function to a series of discrete-valued samples that approximate the original function. Signals can also be broken down into fundamental frequencies, with each frequency having its own intensity. The display of the intensities of all frequency components of a signal is called a power spectrum. However, clinical interest usually lies in signals of a particular frequency range (bandwidth). Filtering the signal is thus required to limit the bandwidth to the appropriate range, in effect filtering out unwanted frequencies. The power density function of surface EMG signals has negligible contributions outside the range 5-10 Hz to 400-450 Hz. The clinically relevant frequency range for EEG signals is usually from 0.1-100 Hz (with the notable exception of the high frequency content of somatosensory evoked potentials). The bandwidth of the filters should therefore be within these ranges.

Thus, EEG and EMG signals are amplified, A/D converted and filtered. One further consideration is the sampling rate of the A/D converter. Digital conversion consists of measuring the voltage at regular intervals and storing the information in digital format (as binary code). The sampling rate is the number of times the signal is measured per second in the A/D conversion process. For a signal whose components vary rapidly with time, if the sampling rate is too low, then these rapid changes may be missed and under-sampled. This leads to the problem of aliasing when higher frequencies can be incorrectly interpreted as lower ones. This is illustrated in figure 2.3. To avoid this ambiguity, the sampling rate should be at least twice that of the highest frequency component of the sample. Put another way, the highest frequency discriminated for a given sampling rate (known as the Nyquist frequency) is equal to half the sampling rate.

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Figure 2.3. The concept of aliasing. A. Sine wave signal. The black dots are the voltages measured at each sampling time as determined by the A/D converter. B. Using the voltages measured in A, a waveform is reconstructed. The frequency of this waveform is much less than that of the original because the sampling time was too slow.

For the acquisition of data in chapters 3-6, EEG and EMG signals were amplified and bandpass filtered with an analogue filter (D150 amplifier, Digitimer, Welwyn Garden City, U.K.). EEG (amplified x 50,000-100,000) was filtered from 0.53 to 300 Hz and EMG (amplified x 1000-5000) from 53 to 300 Hz. After A/D conversion using a 1401

laboratory interface (Cambridge Electronic Design, Cambridge, UK; 12-bit resolution i.e. 4096 [2^^] digital voltage levels for each analogue data point sampled) sampling at 1000 Hz, the data were stored on a personal computer as Spike2 data files (Cambridge Electronic Design, Cambridge, UK). All data were sampled at 1000 Hz, more than double the filter high-cut setting, to avoid aliasing. Prior to analysis, all recorded EEG data was visually assessed off-line to remove any artefact due to eye movements, scalp EMG or mains spikes. For analysis of EEG during muscle activation, only data sections when the muscle was active (as determined by the EMG signal) were used. Similarly, data containing any active EMG were rejected for use as ‘rest’ data. Artefact- free and relevant rest/movement data were exported to new data files using Spike2 software.

In chapter 7, signals were amplified (EEG x 150,000; EMG x 7,500), A/D converted and then digitally filtered (Syn Amps, Neurosoft Inc., U.S.A.). EEG signals were bandpass filtered from 0.3 to 70 Hz and EMG signals from 5 to 100 Hz. The data were sampled at 500 Hz, again high enough to avoid aliasing. After processing, data were stored directly on a personal computer as continuous files using Neuroscan software (Neurosoft Inc., U.S.A.). Eye movement artefact was removed off-line using the ocular artefact reduction script in the Neuroscan software. Ocular artefacts are particularly troublesome for multi-electrode arrays as electrodes placed in the frontal and temporal regions are susceptible to contamination. The Neuroscan software enables the EEG to be ‘corrected’ for eye movements. The ocular artefact reduction script employs a regression analysis in combination with artefact averaging to produce a reliable method for artefact removal (Semlitsch et al., 1986). Firstly, a search is made of the data for maximum eye movement potentials by scanning for the maximum absolute voltage from the VEOG channel. Secondly, an average artefact response is constructed. Averaging is initiated when the voltage exceeds a percentage (in this case 20%) of the maximum eye movement potential. From this average, transmission coefficients are calculated separately for all EEG channels. The electro-oculogram is then subtracted from the EEG on a sweep-by-sweep, point-by-point basis. An example of EEG data with ocular artefact removed using this method is given in figure 2.4.

Data were then converted into .eeg files (software developed by A. Pogosyan, Sobell) for further off-line analysis, including the removal of further artefact (scalp EMG, mains spikes). Prior to all off-line analysis, a 50 Hz digital notch filter was applied to the data to reduce any mains interference.

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Figure 2.4. An example of raw EEG data recorded from a stroke patient (chapter 7) before and after removal of ocular artefact. Linked mastoid electrodes served as the reference.