Towards Neural Decoding of Imagined Speech based on Spoken Speech

Decoding imagined speech from human brain signals is a challenging and important issue that may enable human communication via brain signals. While imagined speech can be the paradigm for silent communication via brain signals, it is always hard to collect enough stable data to train the decoding model. Meanwhile, spoken speech data is relatively easy and to obtain, implying the significance of utilizing spoken speech brain signals to decode imagined speech. In this paper, we performed a preliminary analysis to find out whether if it would be possible to utilize spoken speech electroencephalography data to decode imagined speech, by simply applying the pre-trained model trained with spoken speech brain signals to decode imagined speech. While the classification performance of imagined speech data solely used to train and validation was 30.5 %, the transferred performance of spoken speech based classifier to imagined speech data displayed average accuracy of 26.8 % which did not have statistically significant difference compared to the imagined speech based classifier (p = 0.0983, chi-square = 4.64). For more comprehensive analysis, we compared the result with the visual imagery dataset, which would naturally be less related to spoken speech compared to the imagined speech. As a result, visual imagery have shown solely trained performance of 31.8 % and transferred performance of 26.3 % which had shown statistically significant difference between each other (p = 0.022, chi-square = 7.64). Our results imply the potential of applying spoken speech to decode imagined speech, as well as their underlying common features.Comment: 4 pages, 2 figure

Mobile Kink Solitons in a Van der Waals Charge-Density-Wave Layer

Kinks, point-like geometrical defects along dislocations, domain walls, and DNA, are stable and mobile, as solutions of a sine-Gordon wave equation. While they are widely investigated for crystal deformations and domain wall motions, electronic properties of individual kinks have received little attention. In this work, electronically and topologically distinct kinks are discovered along electronic domain walls in a correlated van der Waals insulator of 1$T$-TaS$_2$. Mobile kinks and antikinks are identified as trapped by pinning defects and imaged in scanning tunneling microscopy. Their atomic structures and in-gap electronic states are unveiled, which are mapped approximately into Su-Schrieffer-Heeger solitons. The twelve-fold degeneracy of the domain walls in the present system guarantees an extraordinarily large number of distinct kinks and antikinks to emerge. Such large degeneracy together with the robust geometrical nature may be useful for handling multilevel information in van der Waals materials architectures.Comment: 12 pages, 4 figure

Incidence, Predictors, Treatment, and Long-Term Prognosis of Patients With Restenosis After Drug-Eluting Stent Implantation for Unprotected Left Main Coronary Artery Disease

ObjectivesThe aim of this study was to evaluate the incidence, predictors, and long-term outcomes of patients with in-stent restenosis (ISR) after percutaneous coronary intervention (PCI) with drug-eluting stents (DES) for unprotected left main coronary artery (LMCA) disease.BackgroundFew data on the clinical course and management of patients experiencing restenosis after DES treatment for unprotected LMCA disease have appeared.MethodsBetween February 2003 and November 2007, 509 consecutive patients with unprotected LMCA disease underwent DES implantation, with 402 (80.1%) undergoing routine surveillance or clinically driven angiographic follow-up. A major adverse cardiac event was defined as the composite of death, myocardial infarction (MI), or target-lesion revascularization.ResultsThe overall incidence of angiographic ISR in LMCA lesions was 17.6% (71 of 402 patients, 57 with focal-type and 14 with diffuse-type ISR. Forty patients (56.3%) underwent repeated PCI, 10 (14.1%) underwent bypass surgery, and 21 (29.6%) were treated medically. During long-term follow-up (a median of 31.7 months), there were no deaths, 1 (2.2%) MI, and 6 (9.5%) repeated target-lesion revascularization cases. The incidence of major adverse cardiac event was 14.4% in the medical group, 13.6% in the repeated PCI group, and 10.0% in the bypass surgery group (p = 0.91). Multivariate analysis showed that the occurrence of DES-ISR did not affect the risk of death or MI.ConclusionsThe incidence of ISR was 17.7% after DES stenting for LMCA. The long-term clinical prognosis of patients with DES-ISR associated with LMCA stenting might be benign, given that these patients were optimally treated with the clinical judgment of the treating physician

Long-Term Clinical Outcomes of Sirolimus- Versus Paclitaxel-Eluting Stents for Patients With Unprotected Left Main Coronary Artery Disease Analysis of the MAIN-COMPARE (Revascularization for Unprotected Left Main Coronary Artery Stenosis: Comparison of Percutaneous Coronary Angioplasty Versus Surgical Revascularization) Registry

ObjectivesThe aim of this study was to evaluate long-term clinical outcomes after implantation of sirolimus-eluting stents (SES) or paclitaxel-eluting stents (PES) among patients with unprotected left main coronary artery (LMCA) disease.BackgroundThere have been few comparisons of long-term outcomes among currently available drug-eluting stents (DES) for the treatment of LMCA disease.MethodsA total of 858 consecutive patients with unprotected LMCA stenosis were treated with SES (n = 669) or PES (n = 189) between May 2003 and June 2006. Primary outcome was the composite of death, myocardial infarction (MI), or target vessel revascularization (TVR).ResultsBaseline clinical and angiographic characteristics were similar in the 2 groups. During 3 years of follow-up, the adjusted risk of primary composite outcome was similar among the groups (SES vs. PES: 25.8% vs. 25.7%, hazard ratio [HR]: 0.95, 95% confidence interval [CI]: 0.64 to 1.41, p = 0.79). The 2 groups also showed a comparable adjusted rate of each component of outcome: death (9.1% vs. 11.0%, HR: 0.92, 95% CI: 0.47 to 1.80, p = 0.82), MI (8.1% vs. 8.0%, HR: 0.80, 95% CI: 0.43 to 1.48, p = 0.47), and TVR (12.1% vs. 10.6%, HR: 1.10, 95% CI: 0.53 to 2.29, p = 0.81). The 3-year rates of definite or probable stent thrombosis were 0.6% in the SES group and 1.6% in the PES group (adjusted p = 0.18).ConclusionsIn consecutive patients with unprotected LMCA disease undergoing DES implantation, SES and PES showed similar long-term clinical outcomes in terms of death, MI, repeat revascularization, and stent thrombosis

Brain-Driven Representation Learning Based on Diffusion Model

Interpreting EEG signals linked to spoken language presents a complex challenge, given the data's intricate temporal and spatial attributes, as well as the various noise factors. Denoising diffusion probabilistic models (DDPMs), which have recently gained prominence in diverse areas for their capabilities in representation learning, are explored in our research as a means to address this issue. Using DDPMs in conjunction with a conditional autoencoder, our new approach considerably outperforms traditional machine learning algorithms and established baseline models in accuracy. Our results highlight the potential of DDPMs as a sophisticated computational method for the analysis of speech-related EEG signals. This could lead to significant advances in brain-computer interfaces tailored for spoken communication

Enhanced Generative Adversarial Networks for Unseen Word Generation from EEG Signals

Recent advances in brain-computer interface (BCI) technology, particularly based on generative adversarial networks (GAN), have shown great promise for improving decoding performance for BCI. Within the realm of Brain-Computer Interfaces (BCI), GANs find application in addressing many areas. They serve as a valuable tool for data augmentation, which can solve the challenge of limited data availability, and synthesis, effectively expanding the dataset and creating novel data formats, thus enhancing the robustness and adaptability of BCI systems. Research in speech-related paradigms has significantly expanded, with a critical impact on the advancement of assistive technologies and communication support for individuals with speech impairments. In this study, GANs were investigated, particularly for the BCI field, and applied to generate text from EEG signals. The GANs could generalize all subjects and decode unseen words, indicating its ability to capture underlying speech patterns consistent across different individuals. The method has practical applications in neural signal-based speech recognition systems and communication aids for individuals with speech difficulties.Comment: 5 pages, 2 figure