# Multi-Session Electrocardiogram-Electromyogram Database for User Recognition
); [EMAIL_ADDRESS] (C.T.K. );  
[PERSON]  
1 IT Research Institute, Chosun University, Gwangju 61452, Republic of Korea; [EMAIL_ADDRESS] (J.S.K. ); [EMAIL_ADDRESS] (C.H.S. ); [EMAIL_ADDRESS] (J.M.K. ); [EMAIL_ADDRESS] (J.L. ); [EMAIL_ADDRESS] (Y.-H.B. ); [EMAIL_ADDRESS] (J.L. ); [EMAIL_ADDRESS] (J.L. ); [EMAIL_ADDRESS] (K.-C.K. ); [EMAIL_ADDRESS] (C.T.K. ); [EMAIL_ADDRESS] (C.T.K. );  
[PERSON]  
1 IT Research Institute, Chosun University, Gwangju 61452, Republic of Korea; [EMAIL_ADDRESS] (J.S.K. ); [EMAIL_ADDRESS] (C.H.S. ); [EMAIL_ADDRESS] (J.M.K. ); [EMAIL_ADDRESS] (J.L. ); [EMAIL_ADDRESS] (Y.-H.B. ); [EMAIL_ADDRESS] (J.L. ); [EMAIL_ADDRESS] (J.L. ); [EMAIL_ADDRESS] (K.-C.K. ); [EMAIL_ADDRESS] (C.T.K. ); [EMAIL_ADDRESS] (C.T.K. );  
[PERSON]  
1 IT Research Institute, Chosun University, Gwangju 61452, Republic of Korea; [EMAIL_ADDRESS] (J.S.K. ); [EMAIL_ADDRESS] (C.H.S. ); [EMAIL_ADDRESS] (J.M.K. ); [EMAIL_ADDRESS] (J.L. ); [EMAIL_ADDRESS] (Y.-H.B. ); [EMAIL_ADDRESS] (J.L. ); [EMAIL_ADDRESS] (J.L. ); [EMAIL_ADDRESS] (J.L. ); [EMAIL_ADDRESS] (J.L. ); [EMAIL_ADDRESS] (K.-C.K. ); [EMAIL_ADDRESS] (C.T.K. );  
[PERSON]  
1 IT Research Institute, Chosun University, Gwangju 61452, Republic of Korea; jthe advantages of being unforgeable and variable, biosignals address the challenges of conventional user recognition methods [2]. To measure such biosignals, a sensor must be attached to the body, as shown in Figure 1[3]. ECG is a biosignal originating from the heartbeat, producing a signal composed of PQRST waves. ECG signals can be acquired from both hands and both feet following the standard 12-lead method, as shown in Figure 1a. EEG is a biosignal generated by brain activity and can be measured at specific locations using the international 10-20 system, positioned based on front-back or left-right distance on the skull, as shown in Figure 1b. EMG is a signal that measures the microcurrents generated when a muscle moves, and the signal can be acquired by attaching a sensor to a muscle in the body, as shown in Figure 1c. Since biosignals are measured by attaching sensors to the body, subjects may experience discomfort and repulsion when constructing the database (DB). To conduct user recognition research using biosignals, a substantial DB (comprising a large number of subjects and repetitions) is essential. However, open access benchmark biosignal DBs used in previous studies have typically featured a limited number of subjects and repetitions. Furthermore, despite the fluctuation in biosignals over time, data in most benchmark biosignal DBs are recorded in a single session (usually one day or less), posing the challenge of limited analyzability. To solve this issue, we present two large Chosun University Biosignal Databases (CSU_BIODBs) designed for user recognition in this study. These databases, referred to as CSU_MBDB1 and CSU_MBDB2, encompass datasets that concurrently acquire both ECG and EMG signals. CSU_MBDB1 comprises ECG and EMG data recorded over multiple sessions (2 days or more) while 36 subjects performed six hand gestures at intervals exceeding one day. Similarly, CSU_MBDB2 includes ECG and EMG data recorded across multiple sessions with 58 subjects executing six hand gestures at intervals exceeding one day. To evaluate the effectiveness of constructed DBs, we conducted a user recognition experiment. The paper's structure is outlined as follows: Section 2 analyzes open access benchmark DBs used in conventional biosignal-based user recognition research. Section 3 presents CSU_MBDB1 and CSU_MBDB2, two extensive multi-session electrocardiogram-electromyogram DBs introduced in this study. Section 4 analyzes the usability of the constructed benchmark biosignal DBs, and Section 5 concludes the paper.