General anesthesia, pivotal for surgical procedures, requires precise depth monitoring to mitigate risks ranging from intraoperative awareness to postoperative cognitive impairments. Traditional assessment methods, relying on physiological indicators or behavioral responses, fall short of accurately capturing the nuanced states of unconsciousness. This study introduces a machine learning-based approach to decode anesthesia depth, leveraging EEG data across different anesthesia states induced by propofol and esketamine in rats. Our findings demonstrate the model's robust predictive accuracy, underscored by a novel intra-subject dataset partitioning and a 5-fold cross-validation method. The research diverges from conventional monitoring by utilizing anesthetic infusion rates as objective indicators of anesthesia states, highlighting distinct EEG patterns and enhancing prediction accuracy. Moreover, the model's ability to generalize across individuals suggests its potential for broad clinical application, distinguishing between anesthetic agents and their depths. Despite relying on rat EEG data, which poses questions about real-world applicability, our approach marks a significant advance in anesthesia monitoring.
Animals ; Machine Learning ; Electroencephalography/methods* ; Ketamine/administration & dosage* ; Rats ; Male ; Propofol/administration & dosage* ; Rats, Sprague-Dawley ; Anesthesia, General/methods* ; Brain/physiology* ; Intraoperative Neurophysiological Monitoring/methods*

Depression is increasingly prevalent among adolescents and can profoundly impact their lives. However, the early detection of depression is often hindered by the time-consuming diagnostic process and the absence of objective biomarkers. In this study, we propose a novel approach for depression detection based on an affective brain-computer interface (aBCI) and the resting-state electroencephalogram (EEG). By fusing EEG features associated with both emotional and resting states, our method captures comprehensive depression-related information. The final depression detection model, derived through decision fusion with multiple independent models, further enhances detection efficacy. Our experiments involved 40 adolescents with depression and 40 matched controls. The proposed model achieved an accuracy of 86.54% on cross-validation and 88.20% on the independent test set, demonstrating the efficiency of multimodal fusion. In addition, further analysis revealed distinct brain activity patterns between the two groups across different modalities. These findings hold promise for new directions in depression detection and intervention.
Humans ; Male ; Female ; Adolescent ; Case-Control Studies ; Depression/diagnosis* ; Early Diagnosis ; Rest ; Electroencephalography/methods* ; Brain-Computer Interfaces ; Models, Psychological ; Reproducibility of Results ; Affect/physiology* ; Photic Stimulation/methods* ; Video Recording ; Brain/physiopathology*

Closed-loop neuromodulation, especially using the phase of the electroencephalography (EEG) rhythm to assess the real-time brain state and optimize the brain stimulation process, is becoming a hot research topic. Because the EEG signal is non-stationary, the commonly used EEG phase-based prediction methods have large variances, which may reduce the accuracy of the phase prediction. In this study, we proposed a machine learning-based EEG phase prediction network, which we call EEG phase prediction network (EPN), to capture the overall rhythm distribution pattern of subjects and map the instantaneous phase directly from the narrow-band EEG data. We verified the performance of EPN on pre-recorded data, simulated EEG data, and a real-time experiment. Compared with widely used state-of-the-art models (optimized multi-layer filter architecture, auto-regress, and educated temporal prediction), EPN achieved the lowest variance and the greatest accuracy. Thus, the EPN model will provide broader applications for EEG phase-based closed-loop neuromodulation.
Humans ; Electroencephalography/methods* ; Brain/physiology* ; Machine Learning ; Signal Processing, Computer-Assisted ; Male ; Adult ; Neural Networks, Computer ; Brain Waves/physiology*

Approximately 30%-40% of epilepsy patients do not respond well to adequate anti-seizure medications (ASMs), a condition known as pharmacoresistant epilepsy. The management of pharmacoresistant epilepsy remains an intractable issue in the clinic. Its early prediction is important for prevention and diagnosis. However, it still lacks effective predictors and approaches. Here, a classical model of pharmacoresistant temporal lobe epilepsy (TLE) was established to screen pharmacoresistant and pharmaco-responsive individuals by applying phenytoin to amygdaloid-kindled rats. Ictal electroencephalograms (EEGs) recorded before phenytoin treatment were analyzed. Based on ictal EEGs from pharmacoresistant and pharmaco-responsive rats, a convolutional neural network predictive model was constructed to predict pharmacoresistance, and achieved 78% prediction accuracy. We further found the ictal EEGs from pharmacoresistant rats have a lower gamma-band power, which was verified in seizure EEGs from pharmacoresistant TLE patients. Prospectively, therapies targeting the subiculum in those predicted as "pharmacoresistant" individual rats significantly reduced the subsequent occurrence of pharmacoresistance. These results demonstrate a new methodology to predict whether TLE individuals become resistant to ASMs in a classic pharmacoresistant TLE model. This may be of translational importance for the precise management of pharmacoresistant TLE.
Epilepsy, Temporal Lobe/diagnosis* ; Animals ; Drug Resistant Epilepsy/drug therapy* ; Electroencephalography/methods* ; Rats ; Anticonvulsants/pharmacology* ; Neural Networks, Computer ; Male ; Humans ; Phenytoin/pharmacology* ; Adult ; Disease Models, Animal ; Female ; Rats, Sprague-Dawley ; Young Adult ; Convolutional Neural Networks

A brain-computer interface (BCI) based on motor imagery (MI) provides additional control pathways by decoding the intentions of the brain. MI ability has great intra-individual variability, and the majority of MI-BCI systems are unable to adapt to this variability, leading to poor training effects. Therefore, prediction of MI ability is needed. In this study, we propose an MI ability predictor based on multi-frequency EEG features. To validate the performance of the predictor, a video-guided paradigm and a traditional MI paradigm are designed, and the predictor is applied to both paradigms. The results demonstrate that all subjects achieved > 85% prediction precision in both applications, with a maximum of 96%. This study indicates that the predictor can accurately predict the individuals' MI ability in different states, provide the scientific basis for personalized training, and enhance the effect of MI-BCI training.
Humans ; Imagination/physiology* ; Electroencephalography/methods* ; Brain-Computer Interfaces ; Male ; Female ; Adult ; Young Adult ; Brain/physiology* ; Movement/physiology* ; Motor Activity/physiology* ; Psychomotor Performance/physiology*

Working memory is an executive memory process that includes encoding, maintenance, and retrieval. These processes can be modulated by transcranial alternating current stimulation (tACS) with sinusoidal waves. However, little is known about the impact of the rate of current change on working memory. In this study, we aimed to investigate the effects of two types of tACS with different rates of current change on working memory performance and brain activity. We applied a randomized, single-blind design and divided 81 young participants who received triangular wave tACS, sinusoidal wave tACS, or sham stimulation into three groups. Participants performed n-back tasks, and electroencephalograms were recorded before, during, and after active or sham stimulation. Compared to the baseline, working memory performance (accuracy and response time) improved after stimulation under all stimulation conditions. According to drift-diffusion model analysis, triangular wave tACS significantly increased the efficiency of non-target information processing. In addition, compared with sham conditions, triangular wave tACS reduced alpha power oscillations in the occipital lobe throughout the encoding period, while sinusoidal wave tACS increased theta power in the central frontal region only during the later encoding period. The brain network connectivity results showed that triangular wave tACS improved the clustering coefficient, local efficiency, and node degree intensity in the early encoding stage, and these parameters were positively correlated with the non-target drift rate and decision starting point. Our findings on how tACS modulates working memory indicate that triangular wave tACS significantly enhances brain network connectivity during the early encoding stage, demonstrating an improvement in the efficiency of working memory processing. In contrast, sinusoidal wave tACS increased the theta power during the later encoding stage, suggesting its potential critical role in late-stage information processing. These findings provide valuable insights into the potential mechanisms by which tACS modulates working memory.
Humans ; Memory, Short-Term/physiology* ; Male ; Female ; Young Adult ; Transcranial Direct Current Stimulation/methods* ; Brain/physiology* ; Adult ; Electroencephalography ; Single-Blind Method

The pain-evoked potential electroencephalogram (EEG) is an effective electrophysiological indicator for pain assessment, yet its extraction is challenging due to interference from background activity and involuntary blinks. Although existing blink artifact-removal methods show efficacy, they face limitations such as the need for reference signals, neglect of individual differences, and reliance on user input, hindering their practical application in clinical pain assessments. In this paper, we propose a novel framework applying adaptive quadrature mirror filter banks (AQMFB) with discrete wavelet transform (DWT) to remove blink artifacts in pain EEG. Unlike traditional DWT methods that apply fixed wavelets across subjects, our method adapts wavelet construction based on the characteristics of EEG. Experimental results demonstrate that AQMFB-DWT outperforms four leading methods in removing blink artifacts with minimal distortion of pain information, all within an acceptable processing time. This technique is a valuable preprocessing step for enhancing the extraction of pain-evoked potentials.
Humans ; Artifacts ; Blinking/physiology* ; Electroencephalography/methods* ; Pain/diagnosis* ; Male ; Wavelet Analysis ; Adult ; Female ; Evoked Potentials/physiology* ; Young Adult ; Brain/physiopathology* ; Pain Measurement/methods* ; Signal Processing, Computer-Assisted

In order to improve the effect of transcranial electrical stimulation treatment and realize personalized treatment for patients with varying severity levels, this paper designed an integrated four-channel EEG recording multimodal transcranial electrical stimulation system. This system can conduct real-time monitoring on EEG and related characteristic analysis before stimulation, in stimulation, and after stimulation. This enables physicians and researchers to resolve real-time brain states, evaluate transcranial electrical stimulation effect, and then artificially adjust the stimulation parameters. After relevant testing and verification, the system can select four stimulation modes: TACS, TDCS, TPCS and TRNS, which can output the constant stimulation current of 0.03 mA accuracy in the range of ±2 mA and the stimulation frequency of low frequency of 0~4 kHz (precision of 0.01 Hz) and high frequency 50~100 kHz, which can obtain more accurate EEG signals under stimulation interference, demonstrating a good market application prospect.
Electroencephalography/methods* ; Transcranial Direct Current Stimulation/instrumentation* ; Humans ; Equipment Design

Neural oscillatory changes play a critical role in pain and analgesia research. Previous studies on pain-related neural oscillations have primarily utilized electroencephalogram (EEG) power spectral analysis, revealing a strong correlation between alpha ( α) power and subjective pain perception. However, alpha power may be influenced by the baseline of the power spectrum, making it difficult to accurately capture the true changes in alpha oscillations. This study employed power spectral analysis and further applied a power spectral parameterization method, which decomposed the power spectrum into periodic and aperiodic components, to compare EEG α power in 50 primiparous women who underwent severe pain during the first stage of labor before and after epidural analgesia. The results indicated no significant differences in α power between pre- and post-analgesia conditions. However, following power spectral parameterization, the aperiodic component of the EEG significantly decreased after analgesia, whereas the periodic component of α power showed a significant increase. This study not only validates the effectiveness and validity of the power spectral parameterization method in analgesia research but also uncovers the differential regulatory mechanism by which analgesia modulates the periodic and aperiodic components of α oscillations.
Humans ; Electroencephalography/methods* ; Female ; Adult ; Alpha Rhythm ; Pregnancy ; Young Adult ; Analgesia, Epidural

OBJECTIVES: To explore the relationship between the Observer's Assessment of Alertness/Sedation (OAAS) score and the bispectral index (BIS) during propofol titration for general anesthesia induction and analyze the impact of BIS monitoring delay on anesthetic depth assessment. METHODS: This study was conducted among 90 patients (ASA class I-II) undergoing elective surgery under general anesthesia. For anesthesia induction, the patients received propofol titration at the rate of 0.5 mg·kg^-1·min^-1 till OAAS scores of 4, 3, 2, and 1 were reached. After achieving an OAAS score of 1, remifentanil (2 μg·kg⁻¹) and rocuronium (0.6 mg·kg⁻¹) were administered, and tracheal intubation was performed 2 min later. BIS values, mean arterial pressure (MAP), heart rate (HR), and propofol dosage at each OAAS score were recorded, and the correlation between OAAS scores and BIS values was analyzed. The diagnostic performance of BIS values for determining when the OAAS score reaches 1 was analyzed using ROC curve. RESULTS: All the patients successfully completed tracheal intubation. BIS values of the patients at each of the OAAS scores differed significantly (P<0.01), and the mean BIS value decreased by 4.08, 8.32, 5.43 and 5.24 as the OAAS score decreased from 5 to 4, from 4 to 3, from 3 to 2, and from 2 to 1, respectively. There was a significant correlation between the OAAS score and BIS values (ρ=0.775, P<0.001). The median BIS value for an OAAS score of 1 was 76, at which point 83.33% of the patients had BIS values exceeding 60. ROC curve analysis showed that for determining an OAAS score of 1, BIS value, at the optimal cutoff value of 84, had a sensitivity of 88.9%, a specificity of 73.3%, and an area under the curve of 0.842 (0.803-0.881). CONCLUSIONS OAAS score during induction of general anesthesia is strongly correlated with BIS value and is a highly sensitive and timely indicator to compensate for the delay in BIS monitoring.
Humans ; Propofol/administration & dosage* ; Male ; Female ; Middle Aged ; Anesthesia, General/methods* ; Adult ; Consciousness Monitors ; Aged ; Young Adult ; Monitoring, Intraoperative/methods* ; Electroencephalography