Recent advances in scalp electroencephalography (EEG) as a neuroimaging tool have now allowed researchers to overcome technical challenges and movement restrictions typical in traditional neuroimaging studies.  Fortunately, recent mobile EEG devices have enabled studies involving cognition and motor control in natural environments that require mobility, such as during art perception and production in a museum setting, and during locomotion tasks.

This dataset is associated with the manuscript entitled "Data-efficient Human Walking Speed Intent Inference". The data represent the measurements taken from 15 able-bodied human subjects as the made speed changes while walking on a treadmill. Each subject is associated with a .mat file that contains 8 variables. Four variables are associated with the training dataset while four are associated with the experimental testing protocol.

This study examined the effectiveness of a detection-based lie detection method that determines lying conditions based on facial autonomic reactions. This technique combines with two other lie detection techniques using a multi sensor fusion technique that is used in the polygraph test to differentiate moments of participants lying and telling the truth about a picked-up card from a deck of cards. Experiments were conducted with 19 participants sitting in front of a camera connected to Galvanic Skin Response (GSR) probes and ECG probes for a polygraph test. 

Physiological database including 7 pairs of facial surface EMG (left/right frontalis, left/right zygomaticus, left/right orbicularis and centre corrugator) muscles, forehead PPG, IMU (Accelerometer, Gyroscope, Magnetometer)  and continuous self-ratings of arousal and valence while watching video stimuli. 37 participants watched an average of 23 minutes worth of videos. This included blocks of positive, negative and neutral segments. Each segment included 10 videos, 30 seconds long each. Relaxation video was played between each segment.