Little finitistic dimensions and generalized derived categories

In this paper, we introduced a generalization of a derived category, which is called $n$-derived category and denoted by $D_{n}(R)$, of a given ring $R$ for each $n\in\mathbb{N}\cup\{\infty\}$. The $n$-derived category of a ring is proved to be very closely connected with its left little finitistic dimension. We also introduce and investigate the notions of $n$-exact sequences, $n$-projective (resp., $n$-injective) modules and $n$-exact complexes. In particular, we characterize the left little finitistic dimensions in terms of all above notions. Besides, the $n$-global dimension $n$-gldim$(R)$ of $R$ is introduced and investigated. Finally, we build a connection of the classical derived categories and $n$-derived categories

An Attention-Based Multi-Domain Bi-Hemisphere Discrepancy Feature Fusion Model for EEG Emotion Recognition

Electroencephalogram (EEG)-based emotion recognition has become a research hotspot in the field of brain-computer interface. Previous emotion recognition methods have overlooked the fusion of multi-domain emotion-specific information to improve performance, and faced the challenge of insufficient interpretability. In this paper, we proposed a novel EEG emotion recognition model that combined the asymmetry of the brain hemisphere, and the spatial, spectral, and temporal multi-domain properties of EEG signals, aiming to improve emotion recognition performance. Based on the 10-20 standard system, a global spatial projection matrix (GSPM) and a bi-hemisphere discrepancy projection matrix (BDPM) are constructed. A dual-stream spatial-spectral-temporal convolution neural network is designed to extract depth features from the two matrix paradigms. Finally, the transformer-based fusion module is used to learn the dependence of fused features, and to retain the discriminative information. We conducted extensive experiments on the SEED, SEED-IV, and DEAP public datasets, achieving excellent average results of 98.33/2.46&lt;inline-formula&gt;&lt;tex-math notation="LaTeX"&gt;$\%$&lt;/tex-math&gt;&lt;/inline-formula&gt;, 92.15/5.13&lt;inline-formula&gt;&lt;tex-math notation="LaTeX"&gt;$\%$&lt;/tex-math&gt;&lt;/inline-formula&gt;, 97.60/1.68&lt;inline-formula&gt;&lt;tex-math notation="LaTeX"&gt;$\%$&lt;/tex-math&gt;&lt;/inline-formula&gt;(valence), and 97.48/1.42&lt;inline-formula&gt;&lt;tex-math notation="LaTeX"&gt;$\%$&lt;/tex-math&gt;&lt;/inline-formula&gt;(arousal) respectively. Visualization analysis supports the interpretability of the model, and ablation experiments validate the effectiveness of multi-domain and bi-hemisphere discrepancy information fusion.</p

Interferential Current Stimulation for Non-Invasive Somatotopic Sensory Feedback for Upper-Limb Prosthesis: Simulation Results using a Computable Human Phantom

The addition of sensory feedback to upper-limb prostheses has been shown to improve several aspects of the user experience. In an attempt to create an intuitive sensory feedback method, transcutaneous electrical stimulation of the stump has been used to elicit referred sensation in the phantom hand by stimulating the underlying nerves. However, the sensation at the electrodes is always reported due to the stimulation of mechanoreceptors. This work investigates the use of interferential stimulation (the superposition of two kilohertz-frequency stimulation currents to form a low-frequency envelope stimulation waveform) to produce focused and selective stimulation that reduces the sensation at the electrodes. A computable human arm phantom model was used to analyse the electric fields created by interferential stimulation against those created by low-frequency stimulation. The results support the assumption that interferential stimulation could result in reduced sensation at the electrode. However, they did not show benefits in terms of penetration at the frequency range considered. In fact, the results suggest that slightly higher currents may be require

An Attention-Based Multi-Domain Bi-Hemisphere Discrepancy Feature Fusion Model for EEG Emotion Recognition

Electroencephalogram (EEG)-based emotion recognition has become a research hotspot in the field of brain-computer interface. Previous emotion recognition methods have overlooked the fusion of multi-domain emotion-specific information to improve performance, and faced the challenge of insufficient interpretability. In this paper, we proposed a novel EEG emotion recognition model that combined the asymmetry of the brain hemisphere, and the spatial, spectral, and temporal multi-domain properties of EEG signals, aiming to improve emotion recognition performance. Based on the 10-20 standard system, a global spatial projection matrix (GSPM) and a bi-hemisphere discrepancy projection matrix (BDPM) are constructed. A dual-stream spatial-spectral-temporal convolution neural network is designed to extract depth features from the two matrix paradigms. Finally, the transformer-based fusion module is used to learn the dependence of fused features, and to retain the discriminative information. We conducted extensive experiments on the SEED, SEED-IV, and DEAP public datasets, achieving excellent average results of 98.33/2.46&lt;inline-formula&gt;&lt;tex-math notation="LaTeX"&gt;$\%$&lt;/tex-math&gt;&lt;/inline-formula&gt;, 92.15/5.13&lt;inline-formula&gt;&lt;tex-math notation="LaTeX"&gt;$\%$&lt;/tex-math&gt;&lt;/inline-formula&gt;, 97.60/1.68&lt;inline-formula&gt;&lt;tex-math notation="LaTeX"&gt;$\%$&lt;/tex-math&gt;&lt;/inline-formula&gt;(valence), and 97.48/1.42&lt;inline-formula&gt;&lt;tex-math notation="LaTeX"&gt;$\%$&lt;/tex-math&gt;&lt;/inline-formula&gt;(arousal) respectively. Visualization analysis supports the interpretability of the model, and ablation experiments validate the effectiveness of multi-domain and bi-hemisphere discrepancy information fusion.</p

Speech decoding from stereo-electroencephalography (sEEG) signals using advanced deep learning methods

Objective: Brain-computer interfaces (BCIs) are technologies that bypass damaged or disrupted neural pathways and directly decode brain signals to perform intended actions. BCIs for speech have the potential to restore communication by decoding the intended speech directly. Many studies have demonstrated promising results using invasive micro-electrode arrays and electrocorticography. However, the use of stereo-electroencephalography (sEEG) for speech decoding has not been fully recognized. Approach: In this research, recently released sEEG data were used to decode Dutch words spoken by epileptic participants. We decoded speech waveforms from sEEG data using advanced deep-learning methods. Three methods were implemented: a linear regression method, an recurrent neural network (RNN)-based sequence-to-sequence model (RNN), and a transformer model. Main results: Our RNN and transformer models outperformed the linear regression significantly, while no significant difference was found between the two deep-learning methods. Further investigation on individual electrodes showed that the same decoding result can be obtained using only a few of the electrodes. Significance: This study demonstrated that decoding speech from sEEG signals is possible, and the location of the electrodes is critical to the decoding performance.</p

Extracting Human-Exoskeleton Interaction Torque for Cable-Driven Upper-Limb Exoskeleton Equipped With Torque Sensors

Powered exoskeletons have global trends in broad applications, such as rehabilitation and human strength amplification in industry, military, and activities of daily livings. The motion intention of the exoskeleton wearer can be obtained using the interaction force at the physical human-machine interface. This article implements joint torque sensors in a custom-made cable-driven exoskeleton. The model of the torque sensor signal is established to extract the human-exoskeleton interaction (HEI) torque, which can be used to predict the human upper-limb motion intention. To accurately decouple the HEI torque from other components in the torque sensor signal, a nonlinear numerical friction model composed of the cable and joint parts is investigated based on the LuGre friction model. A protocol for parameter identification of the proposed friction model is verified experimentally. Furthermore, a coefficient combining the two friction models is designed for antagonistic directions in a joint to account for the bidirectional cable drive's backlash and hysteresis characteristics. Owing to this coefficient, the error of the friction model is reduced by approximately 90% during motion direction change. Finally, the accuracy of the torque sensor model is verified experimentally, and the root-mean-square error (RMSE) is about 0.038 N·m (2.8%). The RMSE of extracted interaction torque is about 0.25 N·m (8.1%). This article validates the feasibility of extracting HEI torque via a torque sensor implemented in the upper-limb exoskeleton, which can promote the development of new generations of upper-limb exoskeleton for active rehabilitation or assistance and research on intuitive control of exoskeleton in future.</p

Speech decoding from stereo-electroencephalography (sEEG) signals using advanced deep learning methods

Objective: Brain-computer interfaces (BCIs) are technologies that bypass damaged or disrupted neural pathways and directly decode brain signals to perform intended actions. BCIs for speech have the potential to restore communication by decoding the intended speech directly. Many studies have demonstrated promising results using invasive micro-electrode arrays and electrocorticography. However, the use of stereo-electroencephalography (sEEG) for speech decoding has not been fully recognized. Approach: In this research, recently released sEEG data were used to decode Dutch words spoken by epileptic participants. We decoded speech waveforms from sEEG data using advanced deep-learning methods. Three methods were implemented: a linear regression method, an recurrent neural network (RNN)-based sequence-to-sequence model (RNN), and a transformer model. Main results: Our RNN and transformer models outperformed the linear regression significantly, while no significant difference was found between the two deep-learning methods. Further investigation on individual electrodes showed that the same decoding result can be obtained using only a few of the electrodes. Significance: This study demonstrated that decoding speech from sEEG signals is possible, and the location of the electrodes is critical to the decoding performance.</p

EFFECTS OF ELECTRICAL MUSCLE STIMULATION ON MYOFASCIALS PAIN SYNDROME:A PRELIMINARY STUDY

Introduction: Many working adults suffer from upper back pain, especially in the trapezius muscle (TM). The myofascial trigger point (MTP) is formed by muscle overload, leading to cell ischemia and contributing to myofascial pain syndrome (MPS). MTPs cause discomfort and reduce muscle function. Current treatments reverse MTPs through com-bination therapy of passive stretching, and trigger point pressure release (TPR), which addresses the long-term effects [1]. However, electrical muscle stimulation (EMS) has the potential to show immediate effects on improving pain by facilitating greater stretch in the affected TM. This study aimed to evaluate the acute effects of EMS on MPS and compare changes in pain intensity (PI), pressure pain threshold (PPT) (both indicators of the MTP sensitivity), and EMG activity in response to different MPS treatments.Methods: Twelve healthy volunteers (4 males and 8 females; age 36.67 ± 12.02 years old), with an MTP in the TM were treated by 5 treatments (T1-T5) applied in a random order: (T1) passive stretching in the upper or middle TM sections, (T2) TPR, (T3) TPR combined with active TM stretching, (T4) active TM stretching with shoulder depression or scapular protraction combined with EMS, and (T5) a combination therapy of TPR and T4 (Fig. 1). The EMS consist-ed of a biphasic, stimulation frequency of 20 Hz with pulse width of 100 µs, and constant current amplitude adjusted for comfort level. The electrodes were applied at the opposite end of the MTP location to stimulate muscle contraction in the region. In doing so, this region would produce active resistance to the applied stretch, encouraging a greater stretch of the TM. The treatment was provided for 10s repeated 3 times with a 10s interval in between. After each treatment the participant rested for 2 minutes. sEMG recording of the MTP region of TM were taken during TM stretching and TM contraction (shoulder elevation or scapular retraction) to evaluate difference in magnitude and frequency of EMG. Differences in PI, PPT and EMG as a result of each treatment were evaluated using ANOVA. Results: Post T4, significant differences were observed in PI (t (11) = -4.022, p = .002) and PPT (t (11) = 4.492, p &lt; .001). Median frequency (MDF) significantly increased during TM action after treatments, but there was no difference amplitude (RMS) (Tab. 1). Combination therapy showed a significant decrease in RMS during TM stretching (F (4, 52) = 15.456, p &lt; .001).Discussion and conclusion: The results indicated that EMS applied during stretching has a significant acute effect on MPS, as evidenced by a reduction in pain (decreased PI and increased PPT) and an increase in EMG frequency during TM action which suggests improved muscle performance, and cell perfusion. Combination therapy showed promise in reducing MTP, which was possibly due to synergistic effects in optimizing muscle contraction which can break energy crisis [2]. However, it is important to note that the data was not segmented by TM sections, limiting the precision of the findings. Future investigations should consider sham stimulation and electrical nerve stimulation.<br/

EFFECTS OF ELECTRICAL MUSCLE STIMULATION ON MYOFASCIALS PAIN SYNDROME:A PRELIMINARY STUDY

Introduction: Many working adults suffer from upper back pain, especially in the trapezius muscle (TM). The myofascial trigger point (MTP) is formed by muscle overload, leading to cell ischemia and contributing to myofascial pain syndrome (MPS). MTPs cause discomfort and reduce muscle function. Current treatments reverse MTPs through com-bination therapy of passive stretching, and trigger point pressure release (TPR), which addresses the long-term effects [1]. However, electrical muscle stimulation (EMS) has the potential to show immediate effects on improving pain by facilitating greater stretch in the affected TM. This study aimed to evaluate the acute effects of EMS on MPS and compare changes in pain intensity (PI), pressure pain threshold (PPT) (both indicators of the MTP sensitivity), and EMG activity in response to different MPS treatments.Methods: Twelve healthy volunteers (4 males and 8 females; age 36.67 ± 12.02 years old), with an MTP in the TM were treated by 5 treatments (T1-T5) applied in a random order: (T1) passive stretching in the upper or middle TM sections, (T2) TPR, (T3) TPR combined with active TM stretching, (T4) active TM stretching with shoulder depression or scapular protraction combined with EMS, and (T5) a combination therapy of TPR and T4 (Fig. 1). The EMS consist-ed of a biphasic, stimulation frequency of 20 Hz with pulse width of 100 µs, and constant current amplitude adjusted for comfort level. The electrodes were applied at the opposite end of the MTP location to stimulate muscle contraction in the region. In doing so, this region would produce active resistance to the applied stretch, encouraging a greater stretch of the TM. The treatment was provided for 10s repeated 3 times with a 10s interval in between. After each treatment the participant rested for 2 minutes. sEMG recording of the MTP region of TM were taken during TM stretching and TM contraction (shoulder elevation or scapular retraction) to evaluate difference in magnitude and frequency of EMG. Differences in PI, PPT and EMG as a result of each treatment were evaluated using ANOVA. Results: Post T4, significant differences were observed in PI (t (11) = -4.022, p = .002) and PPT (t (11) = 4.492, p &lt; .001). Median frequency (MDF) significantly increased during TM action after treatments, but there was no difference amplitude (RMS) (Tab. 1). Combination therapy showed a significant decrease in RMS during TM stretching (F (4, 52) = 15.456, p &lt; .001).Discussion and conclusion: The results indicated that EMS applied during stretching has a significant acute effect on MPS, as evidenced by a reduction in pain (decreased PI and increased PPT) and an increase in EMG frequency during TM action which suggests improved muscle performance, and cell perfusion. Combination therapy showed promise in reducing MTP, which was possibly due to synergistic effects in optimizing muscle contraction which can break energy crisis [2]. However, it is important to note that the data was not segmented by TM sections, limiting the precision of the findings. Future investigations should consider sham stimulation and electrical nerve stimulation.<br/

Augmentation of neovascularization in murine hindlimb ischemia by combined therapy with simvastatin and bone marrow-derived mesenchymal stem cells transplantation

<p>Abstract</p> <p>Objectives</p> <p>We postulated that combining high-dose simvastatin with bone marrow derived-mesenchymal stem cells (MSCs) delivery may give better prognosis in a mouse hindlimb ischemia model.</p> <p>Methods</p> <p>Mouse hindlimb ischemia model was established by ligating the right femoral artery. Animals were grouped (n = 10) to receive local injection of saline without cells (control and simvastatin groups) or with 5 × 10⁶MSCs (MSCs group).Animals received either simvastatin (20 mg/kg/d, simvastatin and combination groups) or saline(control and MSCs group) gavages for continual 21 days. The blood flow was assessed by laser Doppler imaging at day 0,10 and 21 after surgery, respectively. Ischemic muscle was harvested for immunohistological assessments and for VEGF protein detection using western blot assay at 21 days post-surgery. In vitro, MSCs viability was measured by MTT and flow cytometry following culture in serum-free medium for 24 h with or without simvastatin. Release of VEGF by MSCs incubated with different doses of simvastatin was assayed using ELISA.</p> <p>Results</p> <p>Combined treatment with simvastatin and MSCs induced a significant improvement in blood reperfusion, a notable increase in capillary density, a highest level of VEGF protein and a significant decrease in muscle cell apoptosis compared with other groups. In vitro, simvastatin inhibited MSCs apoptosis and increased VEGF release by MSCs.</p> <p>Conclusions</p> <p>Combination therapy with high-dose simvastatin and bone marrow-derived MSCs would augment functional neovascularization in a mouse model of hindlimb ischemia.</p