Data from: Enhancing the security of pattern unlock with surface EMG-based biometrics

Subjects: The data collecting process was approved by the Institutional Review Board (IRB) of the New Mexico Institute of Mining and Technology. We recruited 10 subjects from the school (7 males and 3 females, age = 23.8 ± 2.5 years, height = 173.9 ± 9.7 cm, weight = 70.0 ± 15.6 kg, all right-handed) to participate at the data collecting process. All participants reported that they did not have upper limb musculoskeletal and nervous system diseases. Acquisition: The OpenBCI cyton board was used to collect the sEMG generated by using the right-hand index finger to draw the unlock pattern on an Android mobile phone. The Cyton biosensing board (OpenBCI) recorded finger movement through a gold cup electrode (OpenBCI) placed on the FDS muscle of each subject’s forearm. The sampling rate of the Cyton board is 250 Hz. The acquired sEMG signal is then sent from the Cyton board to the Cyton Dongle (OpenBCI), a Bluetooth adaptor plugged into a laptop. The OpenBCI GUI software installed on the laptop then records the acquired data in the local storage. Each subject was instructed to draw two patterns. Each pattern was drawn repeatedly for 20 trials. sEMG signals were recorded for each trial. The subject was instructed to rest the arm on the table for one minute before the next trial to avoid muscle fatigue. The whole process for a subject lasted about one hour and twenty minutes, which consisted of 40 trials for the two patterns. Pre-processing: The quality of the recorded raw sEMG signals was affected by noises, such as direct current offset, environmental noises, and artifact noises. We applied a 5Hz high-pass filter to eliminate the direct current offset, baseline drift due to the movement in recording, and perspiration. A 60Hz notch filter was then applied to filter out the power-line noise.