About Dataset

Acknowledgments

Please cite as

C.I. Acı, M. Kaya, Y. Mishchenko, "Distinguishing mental attention states of humans via an EEG-based passive BCI using Machine Learning Methods", Expert Systems with Applications, vol. 134, pp. 153-166, 2019.

Context

This is a collection of 34 experiments for monitoring of attention state in human individuals using passive EEG BCI.

Each Matlab file contains the object of the data acquired from EMOTIV device during one experiment. The raw data is contained in o.data, which is array of size {number-of-samples}x25, thus o.data(:,i) comprises one data channel. The sampling frequency is 128 Hz. The list of data channels and their numerical ids is given below per EMOTIV documentation;

1-'ED_COUNTER'
2-'ED_INTERPOLATED'
3-'ED_RAW_CQ'
4-'ED_AF3'
5-'ED_F7'
6-'ED_F3'
7-'ED_FC5'
8-'ED_T7'
9-'ED_P7'
10-'ED_O1'
11-'ED_O2'
12-'ED_P8'
13-'ED_T8'
14-'ED_FC6'
15-'ED_F4'
16-'ED_F8'
17-'ED_AF4'
18-'ED_GYROX'
19-'ED_GYROY'
20-'ED_TIMESTAMP'
21-'ED_ES_TIMESTAMP'
22-'ED_FUNC_ID'
23-'ED_FUNC_VALUE'
24-'ED_MARKER'
25'ED_SYNC_SIGNAL'

The EEG data is in the channels 4:17.