# Multi-Session Electrocardiogram-Electromyogram Database for User Recognition
## 2 Related Work
### Benchmarking Multi-Biosignal DBs  
MeganePro MSD1 [25] involved 15 subjects with transradial amputation and 30 able-bodied subjects, all performing 10 different grasping tasks. During these tasks, data on EMG, video, and gaze tracking were recorded. EMG signals were recorded at a sampling rate of 148 Hz, with eight sensors placed equidistantly from the radio-humeral joint and four sensors positioned 45 mm away. Video and gaze tracking was recorded through eye-tracking glasses. The DEAP dataset [26] captured EEG, electrooculogram (EOG), and EMG signals while 32 subjects watched 40 one-minute videos. Facial images were additionally recorded for twenty-two of the subjects during video observation. The data were obtained at a 512 Hz sampling rate, with 2 channels for EOG sensors positioned vertically and horizontally, 2 channels for EMG sensors on the trapezius and zygomaticus major, and 32 channels for EEG. The recorded biosignals were down-sampled to 128 Hz, and noise was eliminated by applying a 4-45 Hz BPF. The DREAMER dataset [27] is a DB constructed for emotion recognition using EEG and ECG signals. Biosignals from 25 subjects were recorded in response to audio-visual stimuli generated through 18 videos, which portrayed nine different emotions, such as joy, excitement, and happiness, with varying durations ranging from 1 to 393 s. EEG was measured using an Emotiv EPOC wireless EEG headset (EMOTIV, San Francisco, CA, USA), covering 14 channels, while ECG was measured using Shimmer2 ECG sensors. In the constructed DB, data from two subjects were omitted due to inappropriate content. Stress Recognition in Automobile Drivers [28] is a DB designed for detecting stress in driving situations. Various driving situations were incorporated, including a rest period (low stress), highway driving (moderate stress), and city driving (high stress). During these scenarios, subjects had their ECG, EMG, and electrodermal activity (EDA) recorded while refraining from listening to the radio. Each subject engaged in driving for durations ranging from 50 to 90 min, with ECG sensors attached to the Lead II positions, the EMG sensors attached to the trapezius, and EDA sensors attached to the left hand and foot. While six subjects had their biosignals recorded during a single driving session, three subjects underwent repeated biosignals over several days. The MAHNOD implicit-tagging DB [29] captures user responses to multimedia content. Thirty subjects wore six video cameras, a head-mounted microphone, a gaze tracker, and sensors measuring ECG, EEG (32 channels), respiration, and skin temperature while watching movies and images. Two experiments were conducted. In the first experiment, participants viewed short videos from movies and were instructed to tag their emotional state using valence and arousal. In the subsequent experiment, images or videos were shown alongside tags, and subjects were prompted to press the green button if they agreed with the displayed tag and the red button if they disagreed. Table 3 shows a summary of the aforementioned benchmarking multi-biosignal DBs. The benchmark DBs written in Table 3 were built for research on prosthesis control, emotion recognition, etc., not for user recognition research. Due to the inconvenience of attaching sensors to the body for biosignal recording, conventional biosignal DBs used in benchmarking for user recognition involve a limited number of subjects, as shown in Tables 1-3. Furthermore, a significant issue arises from the inability to analyze the variability in biosignals due to the data being recorded in a single session. To solve these problems, this study presents extensive multi-session ECG-EMG DBs (CSU_MBDB1 and CSU_MBDB2).