SAM 40: Dataset of 40 subject EEG recordings to monitor the induced-stress while performing Stroop color-word test, arithmetic task, and mirror image recognition task
Rajdeep Ghosh,
Nabamita Deb,
Kaushik Sengupta,
Anurag Phukan,
Nitin Choudhury,
Sreshtha Kashyap,
Souvik Phadikar,
Ramesh Saha,
Pranesh Das,
Nidul Sinha,
Priyanka Dutta
Affiliations
Rajdeep Ghosh
Department of Information Technology, Gauhati University, Guwahati, Assam 781014, India; Corresponding author.
Nabamita Deb
Department of Information Technology, Gauhati University, Guwahati, Assam 781014, India
Kaushik Sengupta
Department of Information Technology, Gauhati University, Guwahati, Assam 781014, India
Anurag Phukan
Department of Information Technology, Gauhati University, Guwahati, Assam 781014, India
Nitin Choudhury
Department of Information Technology, Gauhati University, Guwahati, Assam 781014, India
Sreshtha Kashyap
Department of Information Technology, Gauhati University, Guwahati, Assam 781014, India
Souvik Phadikar
Department of Electrical Engineering, National Institute of Technology, Silchar, Assam 788010, India
Ramesh Saha
Department of Information Technology, Gauhati University, Guwahati, Assam 781014, India
Pranesh Das
Department of Computer Science and Engineering, National Institute of Technology Calicut, Kozhikode, Kerala 673601, India
Nidul Sinha
Department of Electrical Engineering, National Institute of Technology, Silchar, Assam 788010, India
Priyanka Dutta
Department of Electronics and Communication Engineering, Gauhati University, Guwahati, Assam 781014, India
This paper presents a collection of electroencephalogram (EEG) data recorded from 40 subjects (female: 14, male: 26, mean age: 21.5 years). The dataset was recorded from the subjects while performing various tasks such as Stroop color-word test, solving arithmetic questions, identification of symmetric mirror images, and a state of relaxation. The experiment was primarily conducted to monitor the short-term stress elicited in an individual while performing the aforementioned cognitive tasks. The individual tasks were carried out for 25 s and were repeated to record three trials. The EEG was recorded using a 32-channel Emotiv Epoc Flex gel kit. The EEG data were then segmented into non-overlapping epochs of 25 s depending on the various tasks performed by the subjects. The EEG data were further processed to remove the baseline drifts by subtracting the average trend obtained using the Savitzky-Golay filter. Furthermore, the artifacts were also removed from the EEG data by applying wavelet thresholding. The dataset proposed in this paper can aid and support the research activities in the field of brain-computer interface and can also be used in the identification of patterns in the EEG data elicited due to stress.
