Neurofeedback Therapy for Enhancing Visual Attention: State-of-the-Art and Challenges

We have witnessed a rapid development of brain-computer interfaces (BCIs) linking the brain to external devices. BCIs can be utilized to treat neurological conditions and even to augment brain functions. BCIs offer a promising treatment for mental disorders, including disorders of attention. Here we review the current state of the art and challenges of attention-based BCIs, with a focus on visual attention. Attention-based BCIs utilize electroencephalograms (EEGs) or other recording techniques to generate neurofeedback, which patients use to improve their attention, a complex cognitive function. Although progress has been made in the studies of neural mechanisms of attention, extraction of attention-related neural signals needed for BCI operations is a difficult problem. To attain good BCI performance, it is important to select the features of neural activity that represent attentional signals. BCI decoding of attention-related activity may be hindered by the presence of different neural signals. Therefore, BCI accuracy can be improved by signal processing algorithms that dissociate signals of interest from irrelevant activities. Notwithstanding recent progress, optimal processing of attentional neural signals remains a fundamental challenge for the development of efficient therapies for disorders of attention

Remote Estimation of Peripheral Oxygen Saturation and Pulse Rate From Facial Analysis Using a Smartphone Camera

Vital sign monitoring is an invaluable tool for healthcare professionals, both in the hospital and at home. Traditional measurement devices provide accurate readings but require physical contact with the patient which often is unsuitable, furthermore contact-based devices have been reported to fail by loosing contact due to movement as severe events occur, therefore, a contactless method is necessary.We hypothesize that, in ideal scenarios, it is possible to estimate both SpO2 and pulse rate using only facial video recorded with a smartphone's front-facing camera. To test this hypothesis, a dataset of 10 healthy subjects performing various breathing patterns while being recorded with a smartphone camera was collected during ideal lighting conditions.Using advanced image and signal processing methods to acquire remote photoplethysmography (rPPG) estimates from a patient's forehead, our proposed method can achieve SpO2 estimation results with Arms = 1.34% (accuracy RMS) and MAE ± STD = 1.26 ± 0.68% (mean average error) across a SpO2 range of 92% to 99% (percentage point SpO2) and pulse rate estimation results with Arms = 3.91 bpm (beats per minute) and MAE ± STD = 3.24±2.11 bpm across a pulse rate range of 60 bpm to 90 bpm. We conclude from these results, that remote vital sign estimation using facial videos recorded entirely with a smartphone camera is possible.</p