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We report new constraints on axion-like particle (ALP) using data corresponding to a sodium iodine target exposure of 3063 kg$\cdot$days from the neutrino elastic scattering observation with NaI (NEON) experiment. A 16.7 kg of thallium-doped sodium iodide target was located 23.7 meters from a 2.8 GW thermal power nuclear reactor. We searched for ALPs produced by high-flux photons by comparing the energy spectra of data collected during reactor-on (1596 kg$\cdot$days exposure) and reactor-off (1467 kg$\cdot$days exposure) periods. No signal consistent with ALP interaction was identified, allowing us to set exclusion limits at the 95% confidence level. Our limits cover previously unexplored regions for both photon couplings (${g_{a\gamma}}$) and electron couplings (${g_{ae}}$) for axion masses around 1 MeV/c$^2$. Notably, the NEON data excludes the unconstrained region identified by laboratory-based searches for photon couplings within the "cosmological triangle" for the first time. The observed 95\% confidence level limits reach as low as ${g_{a\gamma}}$ of 4.33$\times$ 10$^{-8}$ GeV$^{-1}$ and ${g_{ae}}$ of 1.10$\times$ 10$^{-9}$ for axion masses of 1.7 MeV/c$^2$ and 1.0 MeV/c$^2$, respectively., Comment: We have discovered a mistake in the ALP signal generation that artificially enhances our sensitivity. As we need additional time to resolve and update these results, we would like to withdraw this article to avoid any confusion for the readers

Brain-to-speech technology represents a fusion of interdisciplinary applications encompassing fields of artificial intelligence, brain-computer interfaces, and speech synthesis. Neural representation learning based intention decoding and speech synthesis directly connects the neural activity to the means of human linguistic communication, which may greatly enhance the naturalness of communication. With the current discoveries on representation learning and the development of the speech synthesis technologies, direct translation of brain signals into speech has shown great promise. Especially, the processed input features and neural speech embeddings which are given to the neural network play a significant role in the overall performance when using deep generative models for speech generation from brain signals. In this paper, we introduce the current brain-to-speech technology with the possibility of speech synthesis from brain signals, which may ultimately facilitate innovation in non-verbal communication. Also, we perform comprehensive analysis on the neural features and neural speech embeddings underlying the neurophysiological activation while performing speech, which may play a significant role in the speech synthesis works., Comment: 4 pages

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.

Decoding EEG signals for imagined speech is a challenging task due to the high-dimensional nature of the data and low signal-to-noise ratio. In recent years, denoising diffusion probabilistic models (DDPMs) have emerged as promising approaches for representation learning in various domains. Our study proposes a novel method for decoding EEG signals for imagined speech using DDPMs and a conditional autoencoder named Diff-E. Results indicate that Diff-E significantly improves the accuracy of decoding EEG signals for imagined speech compared to traditional machine learning techniques and baseline models. Our findings suggest that DDPMs can be an effective tool for EEG signal decoding, with potential implications for the development of brain-computer interfaces that enable communication through imagined speech., Comment: Accepted to Interspeech 2023

Untapped potential for new forms of human-to-human communication can be found in the active research field of studies on the decoding of brain signals of human speech. A brain-computer interface system can be implemented using electroencephalogram signals because it poses more less clinical risk and can be acquired using portable instruments. One of the most interesting tasks for the brain-computer interface system is decoding words from the raw electroencephalogram signals. Before a brain-computer interface may be used by a new user, current electroencephalogram-based brain-computer interface research typically necessitates a subject-specific adaption stage. In contrast, the subject-independent situation is one that is highly desired since it allows a well-trained model to be applied to new users with little or no precalibration. The emphasis is on creating an efficient decoder that may be employed adaptively in subject-independent circumstances in light of this crucial characteristic. Our proposal is to explicitly apply skip connections between convolutional layers to enable the flow of mutual information between layers. To do this, we add skip connections between layers, allowing the mutual information to flow throughout the layers. The output of the encoder is then passed through the fully-connected layer to finally represent the probabilities of the 13 classes. In this study, overt speech was used to record the electroencephalogram data of 16 participants. The results show that when the skip connection is present, the classification performance improves notably., Comment: 4 pages, 3 figures, IEEE BCI winter conference 2023 accepted

Translating imagined speech from human brain activity into voice is a challenging and absorbing research issue that can provide new means of human communication via brain signals. Endeavors toward reconstructing speech from brain activity have shown their potential using invasive measures of spoken speech data, however, have faced challenges in reconstructing imagined speech. In this paper, we propose NeuroTalk, which converts non-invasive brain signals of imagined speech into the user's own voice. Our model was trained with spoken speech EEG which was generalized to adapt to the domain of imagined speech, thus allowing natural correspondence between the imagined speech and the voice as a ground truth. In our framework, automatic speech recognition decoder contributed to decomposing the phonemes of generated speech, thereby displaying the potential of voice reconstruction from unseen words. Our results imply the potential of speech synthesis from human EEG signals, not only from spoken speech but also from the brain signals of imagined speech., Comment: 9 pages, 4 figures, accepted paper of AAAI 2023 in main track

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 figures

Transformers are groundbreaking architectures that have changed a flow of deep learning, and many high-performance models are developing based on transformer architectures. Transformers implemented only with attention with encoder-decoder structure following seq2seq without using RNN, but had better performance than RNN. Herein, we investigate the decoding technique for electroencephalography (EEG) composed of self-attention module from transformer architecture during imagined speech and overt speech. We performed classification of nine subjects using convolutional neural network based on EEGNet that captures temporal-spectral-spatial features from EEG of imagined speech and overt speech. Furthermore, we applied the self-attention module to decoding EEG to improve the performance and lower the number of parameters. Our results demonstrate the possibility of decoding brain activities of imagined speech and overt speech using attention modules. Also, only single channel EEG or ear-EEG can be used to decode the imagined speech for practical BCIs., Comment: submitted to IEEE BCI Winter Conference

Metaverse provides an alternative platform for human interaction in the virtual world. Since virtual platform holds few restrictions in changing the surrounding environments or the appearance of the avatars, it can serve as a platform that reflects human thoughts or even dreams at least in the metaverse world. When it is merged together with the current brain-computer interface (BCI) technology, which enables system control via brain signals, a new paradigm of human interaction through mind may be established in the metaverse conditions. Recent BCI systems are aiming to provide user-friendly and intuitive means of communication using brain signals. Imagined speech has become an alternative neuro-paradigm for communicative BCI since it relies directly on a person's speech production process, rather than using speech-unrelated neural activity as the means of communication. In this paper, we propose a brain-to-speech (BTS) system for real-world smart communication using brain signals. Also, we show a demonstration of imagined speech based smart home control through communication with a virtual assistant, which can be one of the future applications of brain-metaverse system. We performed pseudo-online analysis using imagined speech electroencephalography data of nine subjects to investigate the potential use of virtual BTS system in the real-world. Average accuracy of 46.54 % (chance level = 7.7 %) and 75.56 % (chance level = 50 %) was acquired in the thirteen-class and binary pseudo-online analysis, respectively. Our results support the potential of imagined speech based smart communication to be applied in the metaverse world., Comment: Submitted to 2022 10th IEEE International Winter Conference on Brain-Computer Interface