BattleSound: A Game Sound Benchmark for the Sound-Specific Feedback Generation in a Battle Game.

A haptic sensor coupled to a gamepad or headset is frequently used to enhance the sense of immersion for game players. However, providing haptic feedback for appropriate sound effects involves specialized audio engineering techniques to identify target sounds that vary according to the game. We propose a deep learning-based method for sound event detection (SED) to determine the optimal timing of haptic feedback in extremely noisy environments. To accomplish this, we introduce the BattleSound dataset, which contains a large volume of game sound recordings of game effects and other distracting sounds, including voice chats from a PlayerUnknown's Battlegrounds (PUBG) game. Given the highly noisy and distracting nature of war-game environments, we set the annotation interval to 0.5 s, which is significantly shorter than the existing benchmarks for SED, to increase the likelihood that the annotated label contains sound from a single source. As a baseline, we adopt mobile-sized deep learning models to perform two tasks: weapon sound event detection (WSED) and voice chat activity detection (VCAD). The accuracy of the models trained on BattleSound was greater than 90% for both tasks; thus, BattleSound enables real-time game sound recognition in noisy environments via deep learning. In addition, we demonstrated that performance degraded significantly when the annotation interval was greater than 0.5 s, indicating that the BattleSound with short annotation intervals is advantageous for SED applications that demand real-time inferences.

Pilot study of a single-channel EEG seizure detection algorithm using machine learning.

OBJECTIVE: Seizures are one of the most common emergencies in the neonatal intensive care unit (NICU). They are identified through visual inspection of electroencephalography (EEG) reports and treated by neurophysiologic experts. To support clinical seizure detection, several feature-based automatic neonatal seizure detection algorithms have been proposed. However, as they were unsuitable for clinical application due to their low accuracy, we developed a new seizure detection algorithm using machine learning for single-channel EEG to overcome these limitations. METHODS: The dataset applied in our algorithm contains EEG recordings from human neonates. A 19-channel EEG system recorded the brain waves of 79 term neonates admitted to the NICU at the Helsinki University Hospital. From these datasets, we selected six patients with conformational seizure annotations for the pilot study and allocated four and two patients for our training and testing datasets, respectively. The presence of seizures in the EEGs was annotated independently by three experts through visual interpretation. We divided the data into epochs of 5 s each and further defined a seizure block to label the annotations from each expert recorded every second. Subsequently, to create a balanced dataset, any data point with a non-seizure label was moved to the training and test dataset. RESULT: The developed principal component feature-extracted machine learning algorithm used 62.5% of the relative time (only 5 s for decision) of the baseline, reaching an area under the ROC curve score of 0.91. The effect of diversified parameters was meticulously examined, and 100 principal components were extracted to optimize the model performance. CONCLUSION: Our machine learning-based seizure detection algorithm exhibited the potential for clinical application in NICUs, general wards, and at home and proved its convenience by requiring only a single channel for implementation.

Sleep spindles are generated in the absence of T-type calcium channel-mediated low-threshold burst firing of thalamocortical neurons.

T-type Ca(2+) channels in thalamocortical (TC) neurons have long been considered to play a critical role in the genesis of sleep spindles, one of several TC oscillations. A classical model for TC oscillations states that reciprocal interaction between synaptically connected GABAergic thalamic reticular nucleus (TRN) neurons and glutamatergic TC neurons generates oscillations through T-type channel-mediated low-threshold burst firings of neurons in the two nuclei. These oscillations are then transmitted from TC neurons to cortical neurons, contributing to the network of TC oscillations. Unexpectedly, however, we found that both WT and KO mice for CaV3.1, the gene for T-type Ca(2+) channels in TC neurons, exhibit typical waxing-and-waning sleep spindle waves at a similar occurrence and with similar amplitudes and episode durations during non-rapid eye movement sleep. Single-unit recording in parallel with electroencephalography in vivo confirmed a complete lack of burst firing in the mutant TC neurons. Of particular interest, the tonic spike frequency in TC neurons was significantly increased during spindle periods compared with nonspindle periods in both genotypes. In contrast, no significant change in burst firing frequency between spindle and nonspindle periods was noted in the WT mice. Furthermore, spindle-like oscillations were readily generated within intrathalamic circuits composed solely of TRN and TC neurons in vitro in both the KO mutant and WT mice. Our findings call into question the essential role of low-threshold burst firings in TC neurons and suggest that tonic firing is important for the generation and propagation of spindle oscillations in the TC circuit.

Deletion of phospholipase C beta4 in thalamocortical relay nucleus leads to absence seizures.

Absence seizures are characterized by cortical spike-wave discharges (SWDs) on electroencephalography, often accompanied by a shift in the firing pattern of thalamocortical (TC) neurons from tonic to burst firing driven by T-type Ca(2+) currents. We recently demonstrated that the phospholipase C beta4 (PLCbeta4) pathway tunes the firing mode of TC neurons via the simultaneous regulation of T- and L-type Ca(2+) currents, which prompted us to investigate the contribution of TC firing modes to absence seizures. PLCbeta4-deficient TC neurons were readily shifted to the oscillatory burst firing mode after a slight hyperpolarization of membrane potential. TC-limited knockdown as well as whole-animal knockout of PLCbeta4 induced spontaneous SWDs with simultaneous behavioral arrests and increased the susceptibility to drug-induced SWDs, indicating that the deletion of thalamic PLCbeta4 leads to the genesis of absence seizures. The SWDs were effectively suppressed by thalamic infusion of a T-type, but not an L-type, Ca(2+) channel blocker. These results reveal a primary role of TC neurons in the genesis of absence seizures and provide strong evidence that an alteration of the firing property of TC neurons is sufficient to generate absence seizures. Our study presents PLCbeta4-deficient mice as a potential animal model for absence seizures.

Rapid and non-destructive spectroscopic method for classifying beef freshness using a deep spectral network fused with myoglobin information.

A simple, novel, rapid, and non-destructive spectroscopic method that employs the deep spectral network for beef-freshness classification was developed. The deep-learning-based model classified beef freshness by learning myoglobin information and reflectance spectra over different freshness states. The reflectance spectra (480-920 nm) were measured from 78 beef samples for 17 days, and the datasets were sorted into three freshness classes based on their pH values. Myoglobin information showed statistically significant differences depending on the freshness; consequently, it was utilized as a crucial parameter for classification. The model exhibited improved performance when the reflectance spectra were combined with the myoglobin information. The accuracy of the proposed model improved to 91.9%, whereas that of the single-spectra model was 83.6%. Further, a high value for the area under the receiver operating characteristic curve (0.958) was recorded. This study provides a basis for future studies on the investigation of myoglobin information associated with meat freshness.

Cav2.3 channels are critical for oscillatory burst discharges in the reticular thalamus and absence epilepsy.

Neurons of the reticular thalamus (RT) display oscillatory burst discharges that are believed to be critical for thalamocortical network oscillations related to absence epilepsy. Ca²+-dependent mechanisms underlie such oscillatory discharges. However, involvement of high-voltage activated (HVA) Ca²+ channels in this process has been discounted. We examined this issue closely using mice deficient for the HVA Ca(v)2.3 channels. In brain slices of Ca(v)2.3⁻/⁻, a hyperpolarizing current injection initiated a low-threshold burst of spikes in RT neurons; however, subsequent oscillatory burst discharges were severely suppressed, with a significantly reduced slow afterhyperpolarization (AHP). Consequently, the lack of Ca(v)2.3 resulted in a marked decrease in the sensitivity of the animal to γ-butyrolactone-induced absence epilepsy. Local blockade of Ca(v)2.3 channels in the RT mimicked the results of Ca(v)2.3⁻/⁻ mice. These results provide strong evidence that Ca(v)2.3 channels are critical for oscillatory burst discharges in RT neurons and for the expression of absence epilepsy.