# Development of an Ergonomic User Interface Design of Calcium Imaging Processing System
## 1 Introduction  
An optical brain-machine interface (O-BMI) system can control external devices using optical imaging of brain activities captured from microscopy, having advantages over traditional electrophysiology-based BMI systems using electrical signals of brain activities. BMI systems have been developed to restore communication for patients with neurologicdiseases such as stroke and amyotrophic lateral sclerosis by extracting useful information of perception, cognition, and action from one or many individual neurons using technologies such as electrophysiological and optical recordings [1; 2]. Various BMI systems based on intracortical electrical signals from hundreds of neurons have been developed [3; 4; 5]. However, the electrophysiological method cannot provide information on cell types, cell identification, and the spatial relationships of neurons [6; 7] nor achieve near-natural motor movement because of the limitation of the amount and robustness of recorded neurons [8]. On the other hand, compared to the traditional electrical method, the calcium-imaging method that visualizes the local concentration of calcium in cells or tissues has various advantages such as high resolution, a comprehensive view of large neural populations, stable recording for a long time, and applicability to freely-behaving animals [9].  
Although various O-BMI systems based on calcium images have been developed and commercialized, it has been reported that the UI designs of the existing O-BMI systems require improvement in terms of convenience and usability. Existing O-BMI systems have been developed by applying new technologies such as deep learning techniques and online image processing algorithms to calcium image data [10; 11]. For example, [PERSON] et al. (2020) proposed an algorithm to extract neuronal activities in real-time from microendoscopy using the convolutional neural network (CNN) method, which can achieve high-quality results produced by popular offline approaches such as extended constrained non-negative matrix factorization (CNMF-E) [10; 12]. However, [PERSON] et al. (2019) identified user needs such as information visualization, intuitive control, and layout optimization by conducting a focus group interview with five experts who had prior experience with O-BMI systems (Miniscope, nVista, and Mosaic) [13]. Additionally, [PERSON] et al. (2016) proposed that new functions such as the integrated real-time data processing of feature extraction, neural coding, and feedback control would improve the effectiveness of an O-BMI system [14].  
An ergonomic UI design of an O-BMI system can be developed by accommodating for user needs and preferred design characteristics and resolving UI design problems identified by usability testing. [PERSON] and [PERSON] (2005) presented UI design development cases for mobile applications by incorporating user needs identified from pilot tests using paper prototypes [15]. [PERSON] et al. (2017) developed an improved UI design of a virtual liver-surgery planning system by identifying usability problems and improvement strategies for its preliminary design [16]. [PERSON] et al. (2019) compared various UI designs of a nuclear power plant using performance measures (speed of task and error rate), subjective rating measures (NASA-TLX scores), and physiological measures (pupil dilation, blink rate, total fixation duration, and fixation rate) [17].  
The present study was intended to develop The UI design of a new O-BMI system with better usability and validate the proposed UI design compared to the UI designs of existing O-BMI systems. An ergonomic UI design was developed based on the needs of users, and then its digital prototype was fabricated. Lastly, the proposed UI design was compared with the UI designs of four existing systems (Miniscope, nVista, Mosaic, and Suite2p) by employing objective and subjective methods of usability testing.