Processing Multielectrode EEG Signals Jorge Stolfi Instituto de - - PowerPoint PPT Presentation

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Processing Multielectrode EEG Signals Jorge Stolfi Instituto de - - PowerPoint PPT Presentation

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Processing Multielectrode EEG Signals Jorge Stolfi Instituto de Computa c ao Universidade Estadual de Campinas (UNICAMP) Caixa Postal 6176 13084-971 Campinas, SP, Brasil stolfi@ic.unicamp.br Workshop NeuroMat 2014, NUMEC-USP,

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Processing Multielectrode EEG Signals

Jorge Stolfi Instituto de Computa¸ c˜ ao Universidade Estadual de Campinas (UNICAMP) Caixa Postal 6176 – 13084-971 Campinas, SP, Brasil stolfi@ic.unicamp.br Workshop NeuroMat 2014, NUMEC-USP, 2014-01-23.

Last modified 2014-01-23 00:06:26 by stolfilocal This work is released under the Creative Commons Attribution-ShareAlike.

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1 Processing Multielectrode EEG Signals

Jorge Stolfi

Computer Science Institute (IC) State University of Campinas (UNICAMP) Campinas, SP, Brasil stolfi@ic.unicamp.br

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The Cortex 2 The human cortex (“grey matter”)

  • About 3 mm thick.
  • About 0.2 m2 area unfolded.
  • About 1011 neurons.

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The Cortex 3 Columnar organization:

  • About 105 columns.
  • About 106 neurons per column.
  • Several layers of neurons with distinct morphology.
  • Axons are generally directed inwards.
  • Neurons in one column tend to fire simultaneously.
  • “White matter” is myelinated axons connecting columns.

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EEG Signals 4 Single neuron firings cannot be detected by EEG:

  • Neural pulses have high voltages (100 mV) but low energy.
  • Multipole potentials decay rapidly (1/r3 or faster).
  • Too many neurons firing at the same time.
  • Neuronal pulses last ∼ 1 ms.
  • Neuron can fire at nearly 200 Hz.

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EEG Signals 5 Column activity can almost be detected:

  • Each neuron firing transports a bit of charge down the axon.
  • Thousands to millions of neurons in a column fire together.
  • Neurons are generally oriented with axon inwards.

Result: significant dipole potential field.

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EEG Signals 6 Dipole current source:

  • Return current distrinuted through medium.
  • Bi-lobe potential distribution.
  • Potential decays like 1/r2.

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EEG Signals 7 Pattern expected on scalp for single dipole source:

  • Spot-and-halo pattern for normal dipole.
  • Bipolar pattern for parallel dipole.
  • Potential decays like 1/r2 away from maximum.
  • Amplitude decays with 1/d2.

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EEG Signals 8 A single column is still too weak to be detected

  • Detecteble only if many adjcent columns fire together.
  • There are 105 columns but only 102 measurements.
  • At best we can detect activity in 102 regions.
  • Unblurring (Laplacian filtering).

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The 128-Electrode EEG Signals 9 Signal data:

  • 1. 128 electrodes + reference (zero potential).
001 002 003 004 005 006 007 008 009 010 011 012 013 014 015 016 017 018 019 020 021 022 023 024 025 026 027 028 029 030 031 032 033 034 035 036 037 038 039 040 041 042 043 044 045 046 047 048 049 050 051 052 053 054 055 056 057 058 059 060 061 062 063 064 065 066 067 068 069 070 071 072 073 074 075 076 077 078 079 080 081 082 083 084 085 086 087 088 089 090 091 092 093 094 095 096 097 098 099 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128
  • 2. Sampling frequency 500 Hz.
  • 3. Typical signal amplitudes < 100 µV.
  • 4. Large and random offsets > 5000 µV.

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The 128-Electrode EEG Signals 10 Raw data for one run:

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The 128-Electrode EEG Signals 11 Power spectrum:

Note: logarithmic power scale.

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Frequency filtering 12 Test signal:

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Frequency filtering 13 Butterworth 8-pole filter:

Note the ringing artifacts.

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Frequency filtering 14 Gaussian filter:

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Frequency filtering 15 Filtered run:

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Frequency filtering 16 Filtered run:

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Frequency filtering 17 Power spectrum of filtered run (s013-00209):

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Frequency filtering 18 Another filtered run:

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Frequency filtering 19 Another filtered run:

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Blink patterns 20 Blinks have a well-defined pattern that can be removed with PCA:

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Pulsating 10 Hz gradient 21 The second PCA component is a pulsating vertical gradient that oscillates irregularly with frequency ∼ 10 Hz:

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Isolating blinks and 10 Hz gradient 22

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Isolating blinks and 10 Hz gradient 23

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Isolating blinks and 10 Hz gradient 24

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Isolating blinks and 10 Hz gradient 25

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Conclusions 26

  • One should always look closely at the data.
  • This EEG data will needs a lot of cleanup and processing

before it can be used for structural analysis.

  • Hardware prevention is better than software cure.

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