Stress classification with in-ear heartbeat sounds

BACKGROUND

Although stress plays a key role in tinnitus and decreased sound tolerance, conventional hearing devices used to manage these conditions are not currently capable of monitoring the wearer's stress level. The aim of this study was to assess the feasibility of stress monitoring with an in-ear device.

METHOD

In-ear heartbeat sounds and clinical-grade electrocardiography (ECG) signals were simultaneously recorded while 30 healthy young adults underwent a stress protocol. Heart rate variability features were extracted from both signals to train classification algorithms to predict stress vs. rest.

RESULTS

Models trained and tested using in-ear heartbeat sounds appeared to perform better than the models trained and tested using the ECG signals. However, further analyses comparing heart rate variability features extracted from ECG and the in-ear heartbeat sounds suggest that the improvement in stress prediction performance was driven by the increased presence of artifacts (e.g. movement or speech) during the stress tasks, rather than physiologically meaningful changes in the heartbeat signals that would be indicative of stress in real-world settings. To address this difference in error between rest and stress conditions, a data augmentation method was proposed to balance the error.

CONCLUSIONS

The final system demonstrates the viability of robust stress recognition with only in-ear heartbeat sounds, which could expand the capabilities of hearing devices used to address conditions related to stress and noise. The proposed data augmentation method effectively identified and addressed artifact-related biases, which could broadly be applied to improve robustness of biosignal monitoring with machine learning.

Several inaccurate or erroneous conceptions and misleading propaganda about brain-computer interfaces

Brain-computer interface (BCI) is a revolutionizing human-computer interaction, which has potential applications for specific individuals or groups in specific scenarios. Extensive research has been conducted on the principles and implementation methods of BCI, and efforts are currently being made to bridge the gap from research to real-world applications. However, there are inaccurate or erroneous conceptions about BCI among some members of the public, and certain media outlets, as well as some BCI researchers, developers, manufacturers, and regulators, propagate misleading or overhyped claims about BCI technology. Therefore, this article summarizes the several misconceptions and misleading propaganda about BCI, including BCI being capable of "mind-controlled," "controlling brain," "mind reading," and the ability to "download" or "upload" information from or to the brain using BCI, among others. Finally, the limitations (shortcomings) and limits (boundaries) of BCI, as well as the necessity of conducting research aimed at countering BCI systems are discussed, and several suggestions are offered to reduce misconceptions and misleading claims about BCI.

Information sparseness in cortical microelectrode channels while decoding movement direction using an artificial neural network

Implanted microelectrode arrays can directly pick up electrode signals from the primary motor cortex (M1) during movement, and brain-machine interfaces (BMIs) can decode these signals to predict the directions of contemporaneous movements. However, it is not well known how much each individual input is responsible for the overall performance of a BMI decoder. In this paper, we seek to quantify how much each channel contributes to an artificial neural network (ANN)-based decoder, by measuring how much the removal of each individual channel degrades the accuracy of the output. If information on movement direction was equally distributed among channels, then the removal of one would have a minimal effect on decoder accuracy. On the other hand, if that information was distributed sparsely, then the removal of specific information-rich channels would significantly lower decoder accuracy. We found that for most channels, their removal did not significantly affect decoder performance. However, for a subset of channels (16 out of 61), removing them significantly reduced the decoder accuracy. This suggests that information is not uniformly distributed among the recording channels. We propose examining these channels further to optimize BMIs more effectively, as well as understand how M1 functions at the neuronal level.