ECG, EEG and IMU data for local motion artefact removal

  • Christopher Beach (Creator)
  • Mingjie Li (Creator)
  • Ertan Balaban (Creator)

Dataset

Description

ECG and EEG data collected from a single participant using our custom device with inertial measurement units (IMUs) placed on each electrode, to enable recording of local motion activity (accelerometer and gyroscope) during electrophysiological recordings. This is to enable improved motion artefact removal. Please see paper for description of the device, placement of the electrodes and testing procedure. Data is in CSV format, with a sample rate of 220 Hz.

If using this data please cite:C. Beach, M. Li, E. Balaban, and A. J. Casson, “Motion artefact removal in electroencephalography and electrocardiography by using multichannel inertial measurement units and adaptive filtering,” Healthc. Technol. Lett., vol. 8, no. 5, pp. 128–138, 2021, doi: 10.1049/htl2.12016.

The columns of the csv files correspond as follows:
ECG:
x: Internal system timestamp
eeg1: V1 electrode
eeg2: V2 electrode
ax1: x-axis of accelerometer on V1
ay1: y-axis of accelerometer on V1
az1: z-axis of accelerometer on V1
gx1: x-axis of gyroscope on V1
gy1: y-axis of gyroscope on V1
gz1: z-axis of gyroscope on V1
ax2: x-axis of accelerometer on V2
ay2: y-axis of accelerometer on V2
az2: z-axis of accelerometer on V2
gx2: x-axis of gyroscope on V2
gy2: y-axis of gyroscope on V2
gz2: z-axis of gyroscope on V2
ax3: x-axis of accelerometer on REF
ay3: y-axis of accelerometer on REF
az3: z-axis of accelerometer on REF
gx3: x-axis of gyroscope on REF
gy3: y-axis of gyroscope on REF
gz3: z-axis of gyroscope on REF
ax4: x-axis of accelerometer on DRL
ay4: y-axis of accelerometer on DRL
az4: z-axis of accelerometer on DRL
gx4: x-axis of gyroscope on DRL
gy4: y-axis of gyroscope on DRL
gz4: z-axis of gyroscope on DRL

EEG:
x: Internal system timestamp
eeg1: T3 electrode
eeg2: T5 electrode
ax1: x-axis of accelerometer on T3
ay1: y-axis of accelerometer on T3
az1: z-axis of accelerometer on T3
gx1: x-axis of gyroscope on T3
gy1: y-axis of gyroscope on T3
gz1: z-axis of gyroscope on T3
ax2: x-axis of accelerometer on T5
ay2: y-axis of accelerometer on T5
az2: z-axis of accelerometer on T5
gx2: x-axis of gyroscope on T5
gy2: y-axis of gyroscope on T5
gz2: z-axis of gyroscope on T5
ax3: x-axis of accelerometer on REF
ay3: y-axis of accelerometer on REF
az3: z-axis of accelerometer on REF
gx3: x-axis of gyroscope on REF
gy3: y-axis of gyroscope on REF
gz3: z-axis of gyroscope on REF
ax4: x-axis of accelerometer on DRL
ay4: y-axis of accelerometer on DRL
az4: z-axis of accelerometer on DRL
gx4: x-axis of gyroscope on DRL
gy4: y-axis of gyroscope on DRL
gz4: z-axis of gyroscope on DRL

In our paper we take plot the following sections of data:
ECG:
Lines 1939:5039 (for the good filtering case)
Lines 7100:10200 (for the poor filtering case)
EEG:
Lines 7279:10439
Date made available20 Sep 2021
PublisherUniversity of Manchester figshare

Keywords

  • EEG data
  • electrocardiograms
  • electroencephalogram (EEG)
  • motion artefacts
  • wearable sensors
  • IMUs
  • inertimeasurement unit (IMU)
  • Adaptive filtering
  • multichannel adaptive filtering
  • ECG
  • Biomedical Engineering not elsewhere classified
  • signal processing

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