Benchmarking of the BITalino biomedical toolkit against an established gold standard

Abstract

The low-cost multimodal platform BITalino is being increasingly used for educational and research purposes. However, there is still a lack of well-structured work comparing data acquired by this toolkit against a reference device, using established experimental protocols. This work intends to fill the said gap by benchmarking the performance of BITalino against the BioPac MP35 Student Lab Pro device. This work followed a methodical experimental protocol to acquire data from the two devices simultaneously. Four physiological signals were acquired: electrocardiography, electromyography, electrodermal activity and electroencephalography. Root mean square error and coefficient of determination were computed to analyse differences between BITalino and BioPac. Electrodermal activity signals were very similar for the two devices, even without applying any major signal processing techniques. For electrocardiography, a simple morphological comparison also revealed high similarity between devices, and this similarity increased after a common segmentation procedure was followed. Regarding electromyography and electroencephalography data, the approach consisted of comparing features extracted using common post-processing methods. The differences between BITalino and BioPac were again small. Overall, the results presented here show a close similarity between data acquired by the BITalino and by the reference device. This is an important validation step for all researchers working with this multimodal platform.

Keywords: BITalino biomedical toolkit; BioPac MP35 Student Lab Pro device; data acquisition; educational research purposes; electrocardiography; electrodermal activity signals; electroencephalography; electroencephalography data; electromyography; electromyography data; feature extraction; mean square error methods; medical signal detection; medical signal processing; methodical experimental protocol; physiological signal acquisition; physiology; post-processing methods; root mean square error; signal processing techniques.

Fig. 1

Biomedical sensors bundled by default in BITalino (r)evolution

Fig. 2

Pulses emitted by the BioPac (top) and acquired BITalino light sensor data (bottom) during an ECG recording, prior to time alignment and synchronisation. Red vertical lines indicate the beginning and end of each one of the four activities

Fig. 3

First 3 s of filtered ECG segments before (top) and after (bottom) the alignment procedure. Segments were scaled for visualisation purposes

Fig. 4

BioPac and BITalino EDA data from one subject, after signal alignment and filtering. Segments were scaled for visualisation purposes

Fig. 5

EMG data (in blue) and computed linear envelope (in red) for one activity

Fig. 6

BioPac and BITalino EEG data from one subject, after signal filtering. Segments were scaled for visualisation purposes

Fig. 7

PSD (left) and scaled PSD (right) for one activity