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.

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.

A Working Prototype Using DS18B20 Temperature Sensor and Arduino for Health Monitoring.

Due to the successful emergence of internet of things, sensor-based real-time health monitoring is getting popularized. A usable health-monitoring system is required for prolonged monitoring of the patient with reduced cost. This paper describes a working prototype system for real-time health-monitoring system using DS18B20 temperature sensor, Arduino Nano with micro-controller ATmega328 where Zigbee module is used for wireless communication. In this prototype sensor data gets acquired and analyzed to give proper feedback to the patient with or without mobility support at indoor. The sensor vitals are collected and sent to the computing device using shielded cable and ZigBee, i.e., through wired and wireless communication, respectively. Analysis of patient vitals based on medical definitions gives patient's real-time health condition so that if condition is not normal, then timely preventive measures can be taken to avoid further complication. Per user data can be saved in the system database for further reference.