Drowsiness is a leading cause of traffic and industrial accidents, costing lives and productivity. Electroencephalography (EEG) signals can reflect awareness and attentiveness, and low-cost consumer EEG headsets are available on the market. The use of these devices as drowsiness detectors could increase the accessibility of safety and productivity-enhancing devices for small businesses and developing countries. We conducted a systemic review of currently available, low-cost, consumer EEG-based drowsiness detection systems. We sought to determine whether consumer EEG headsets could be reliably utilized as rudimentary drowsiness detection systems. We included documented cases describing successful drowsiness detection using consumer EEG-based devices, including the Neurosky MindWave, InteraXon Muse, Emotiv Epoc, Emotiv Insight, and OpenBCI. Of 46 relevant studies, ~27 reported an accuracy score. The lowest of these was the Neurosky Mindwave, with a minimum of 31%. The second lowest accuracy reported was 79.4% with an OpenBCI study. In many cases, algorithmic optimization remains necessary. Different methods for accuracy calculation, system calibration, and different definitions of drowsiness made direct comparisons problematic. However, even basic features, such as the power spectra of EEG bands, were able to consistently detect drowsiness. Each specific device has its own capabilities, tradeoffs, and limitations. Widely used spectral features can achieve successful drowsiness detection, even with low-cost consumer devices; however, reliability issues must still be addressed in an occupational context.

A non-invasive, brain-to-brain interface (BBI) requires precision neuromodulation and high temporal resolution as well as portability to increase accessibility. A BBI is a combination of brain-computer interface (BCI) and computer-brain interface (CBI). The optimization of BCI parameters has been extensively researched, but CBI has not. Parameters taken from the BCI and CBI literature were used to simulate a two-class medical monitoring BBI system under a wide range of conditions. BBI function was assessed using the information transfer rate (ITR), measured in bits per trial and bits per minute. The BBI ITR was a function of classifier accuracy, window update rate, system latency, stimulation failure rate, and timeout threshold. The BCI parameters, including window length, update rate, and classifier accuracy, were kept constant to investigate the effects of varying the CBI parameters, including system latency, stimulation failure rate, and timeout threshold. Based on passively monitoring BCI parameters, a base ITR of 1 bit/trial was used. The optimal latency was found to be 100 ms or less, with a threshold no more than twice its value. With the optimal latency and timeout parameters, the system was able to maintain near-maximum efficiency, even with a 25% stimulation failure rate. When the CBI and BCI parameters are compared, the CBI’s system latency and timeout threshold should be reflected in the BCI’s update rate. This would maximize the number of trials, even at a high stimulation failure rate. These findings suggested that a higher number of trials per minute optimizes the ITR of a non-invasive BBI. The delays innate to each BCI protocol and CBI stimulation method must also be accounted for. The high latencies in each are the primary constraints of non-invasive BBI for the foreseeable future.

Background: The phase correction on transcranial focused ultrasound is essential to regulate unwanted focal point shift caused by skull bone aberration. The aim of the current study was to design and investigate the feasibility of a ray-based phase correction toolkit for transcranial focused ultrasound. Results: The peak pressure at focal area was improved by 140.5 ± 7.0% on target I and 134.8 ± 19.1% on target II using proposed phase correction toolkit, respectively. A total computation time of 402.1 ± 24.5 milliseconds was achieved for each sonication. Conclusion: The designed ray-based phase correction software can be used as a lightweight toolkit to compensate aberrated phase within clinical environment.

Doppler ultrasound is one of the oldest modalities for measuring and visualizing blood flow. This review paper explores the principles of Doppler ultrasound and blood flow imaging. Following a brief history of Doppler ultrasound, all Doppler modes, including B-Flow imaging, are discussed, with emphasis on spectral broadening and Doppler spectrum amplitude in relation to clinical implications. The paper also outlines sources of uncertainty in Doppler flow measurement. Emerging technologies in blood flow imaging are introduced, including microvascular flow imaging without contrast agents and four-dimensional vascular imaging from a two-dimensional transducer, which have been commercialized within the past decade, as well as row-column array transducer systems, high-frame-rate imaging, and photoacoustic imaging, which remain in preclinical and research stages.