using NeuroAnalyzerNeuroAnalyzer tutorials: MEPs
Motor evoked potentials (MEPs) are the electrical signals recorded from the descending motor pathways or from muscles following stimulation of motor pathways within the brain (source: Encyclopedia of the Neurological Sciences).
MEPs are used in Transcranial Magnetic Stimulation (TMS) for determining the intensity of stimulation.
NeuroAnalyzer allows analysis of MEP recordings. Usually recording contains more than one MEP. Each MEP is imported as individual channel of the NeuroAnalyzer.NEURO object.
Load MEP recording exported from DuoMAG stimulator:
mep = import_duomag("mep-duomag.m")or
mep = import_duomag("files/mep-duomag.ascii")m[ Info: Imported: MEP (19 × 8129 × 1; 0.51 s)NeuroAnalyzer.NEURO(NeuroAnalyzer.HEADER(Dict{Symbol, Any}(:weight => -1, :id => "000011", :middle_name => "", :height => -1, :head_circumference => -1, :handedness => "", :last_name => "5-1", :first_name => ""), Dict{Symbol, Any}(:stimulation_sample => [1754, 1754, 1754, 1754, 1754, 1754, 1754, 1754, 1754, 1754, 1754, 1754, 1754, 1754, 1754, 1754, 1754, 1754, 1754], :channel_type => ["mep", "mep", "mep", "mep", "mep", "mep", "mep", "mep", "mep", "mep", "mep", "mep", "mep", "mep", "mep", "mep", "mep", "mep", "mep"], :label => ["MEP1", "MEP2", "MEP3", "MEP4", "MEP5", "MEP6", "MEP7", "MEP8", "MEP9", "MEP10", "MEP11", "MEP12", "MEP13", "MEP14", "MEP15", "MEP16", "MEP17", "MEP18", "MEP19"], :coil_type => ["----", "----", "----", "----", "----", "----", "----", "----", "----", "----", "----", "----", "----", "----", "----", "----", "----", "----", "----"], :data_type => "mep", :recording_notes => "", :markers_pos => [2108, 2126, 2150, 2166, 2168, 2156, 0, 2151, 2094, 2150, 2113, 2158, 0, 0, 0, 0, 0, 2124, 0], :recording_date => "21.11.2019", :sampling_rate => 12500, :file_type => "DuoMAG"…), Dict(:name => "", :design => "", :notes => "")), [-0.1402, -0.1402, -0.1401, -0.14, -0.1399, -0.1398, -0.1398, -0.1397, -0.1396, -0.1395 … 0.5093, 0.5094, 0.5094, 0.5095, 0.5096, 0.5097, 0.5098, 0.5098, 0.5099, 0.51], [-0.1402, -0.1402, -0.1401, -0.14, -0.1399, -0.1398, -0.1398, -0.1397, -0.1396, -0.1395 … 0.5093, 0.5094, 0.5094, 0.5095, 0.5096, 0.5097, 0.5098, 0.5098, 0.5099, 0.51], [-1.3082855504587165 0.6917144495412835 … 2.6917144495412835 3.6917144495412835; -2.2894495412844016 -2.2894495412844016 … -0.2894495412844016 0.7105504587155984; … ; -0.8612672018348633 0.13873279816513673 … -6.861267201834863 -5.861267201834863; -2.539850917431192 0.46014908256880815 … 52.460149082568805 51.460149082568805;;;], Dict{Any, Any}(), 0×5 DataFrame
Row │ id start length value channel
│ String Float64 Float64 String Int64
─────┴───────────────────────────────────────────, 0×9 DataFrame
Row │ label loc_radius loc_theta loc_x loc_y loc_z loc_radius_sp ⋯
│ String Float64 Float64 Float64 Float64 Float64 Float64 ⋯
─────┴──────────────────────────────────────────────────────────────────────────
3 columns omitted, String[])
Show recording details:
info(mep) Data type: MEP
File format: DuoMAG
Source file: files/mep-duomag.ascii
File size [MB]: 0
Memory size [MB]: 1.25
Subject: 000011: 5-1
Recording:
Recording notes:
Recording date: 21.11.2019
Recording time: 14:28:51
Sampling rate (Hz): 12500
Signal length [samples]: 8129
Signal length [seconds]: 0.65
Number of channels: 19
Components: no
Channels:
ch label type unit bad
------ --------------- ----------- ------- -------
1 MEP1 MEP μV false
2 MEP2 MEP μV false
3 MEP3 MEP μV false
4 MEP4 MEP μV false
5 MEP5 MEP μV false
6 MEP6 MEP μV false
7 MEP7 MEP μV false
8 MEP8 MEP μV false
9 MEP9 MEP μV false
10 MEP10 MEP μV false
11 MEP11 MEP μV false
12 MEP12 MEP μV false
13 MEP13 MEP μV false
14 MEP14 MEP μV false
15 MEP15 MEP μV false
16 MEP16 MEP μV false
17 MEP17 MEP μV false
18 MEP18 MEP μV false
19 MEP19 MEP μV false Detect peaks:
mep_peaks(mep)19×2 Matrix{Int64}:
2110 2076
2128 2086
2149 2077
2168 2072
2170 2073
2159 2082
6562 2129
2154 2073
2096 2232
2152 2090
2115 2202
2160 2087
5658 2385
2745 7601
7548 2092
2075 2112
3099 2119
2125 2094
7699 2844(!) The output matrix contains the position (in samples) of the positive (first column) and negative (second column) peaks.
Plot MEP, channel 1:
plot_mep(mep, ch="MEP1")m[ Info: Positive peak time: 28.0 ms
[ Info: Positive peak amplitude: 65.69 μV
[ Info: Negative peak time: 26.0 ms
[ Info: Negative peak amplitude: -95.31 μV(!) These peaks are auto-detected. Files exported from DuoMAG contain peaks detected by the stimulator. To use embedded peaks, use:
plot_mep(mep, ch="MEP1", peaks_detect=false)[ Info: Positive peak time: 28.0 ms
[ Info: Positive peak amplitude: 65.69 μV
[ Info: Negative peak time: 26.0 ms
[ Info: Negative peak amplitude: -96.31 μVButterfly plot over MEPs:
plot_mep(mep, ch="all", type=:butterfly, avg=true)m[ Info: Positive peak time: 32.0 ms
[ Info: Positive peak amplitude: 63.15 μV
[ Info: Negative peak time: 26.0 ms
[ Info: Negative peak amplitude: -96.06 μV