Supernumerary BCI dataset: exploring the imagination of a third arm using BCI
Brain-Computer Interface (BCI) is the technology that enables bodyless communication with machines or devices; this is done using the translation of the electrical activity in the brain (EEG) signals into command outputs. Motor Imagery BCI (MI-BCI) employs the amplitude changes elicited voluntarily by the mental rehearsal of physical motor actions (known as event-related de-synchronization and synchronization - ERD/ERS). MI-BCI applications focuses on mental representations of jointed limbs following the human anatomy constraints (e.g., two arms in a symmetrical distribution), without any exploration or applications that include non-embodied human limbs in BCI systems, even though Rubber Hand Illusion (RHI) experiments demonstrated the human capabilities to create body transfer illusions. In this vein. this dataset is part of the master's degree thesis, which includes a step towards the development of a supernumerary BCI system. The experiment aims to study the feasibility of including a virtual third arm in an MI-BCI system while comparing the effectiveness of using the conventional arrows and fixation cross as a training step (Graz) against a first-person view using a human avatar (Hands). The dataset was used for two studies: an EEG analysis of the induced brain oscillatory activity elicited by the third arm using Event-Related Spectral Perturbation (ERSP); and an offline exploration of the classification of the third arm task. There were two recording sessions with two runs in each one with a resting time between them. The sessions were conducted on two separate days within one week. Ten right-handed volunteers (four women) participated in the study. We collected the EEG data using an OpenBCI 32 bit board at a sampling rate of 250 Hz. Following the 10-20 EEG placement system, eight passive gold cup electrodes were used and placed at the sensorimotor cortex. The experiment involves the execution of four different tasks in two training conditions (both performed in a VR environment). The subjects were invited to rest (RS), or to move (either imaginary or execute when possible) a specific hand: third hand (TH), left hand (LH), and right hand (RH). The Hands condition involved the presentation of a human-like avatar, whereas Graz the presentation of arrows, both following the usual BCI timing protocol. The users performed 20 trials of each task randomly selected with a duration of seven seconds each (see Figure 'Recording_protocol.png). In light of the classification results, it is possible to argue the feasibility of including the virtual third arm into a BCI system. In line with the literature, realistic training enhances the modulation of ERS/ERD patterns, and consequently, the performance of the user in motor imagery tasks; however, it creates an additional cognitive load presumably caused by visual processing.