Here we present the Frontiers in Human Neuroscience ‘Neural Decoding for Direct Communication in Brain-Computer Interface’ article collection. People hope for a future in which a brain-computer interface (BCI) decodes what one intuitively imagines and outputs it to the real-world environment. Once the imagined word or conversation is decoded by the BCI system, it can then be used as a neural command to control external devices (e.g., computers, robots, IoT systems., etc.). Recently, to implement these types of BCI, relevant features for the direct communication paradigm have been investigated, aiming to improve the effectiveness of capturing speech-related brain activity. With the aim of decoding intuitive speech, BCI is evolving in conjunction with significant challenges.
Speech imagery can be a key paradigm for developing intuitive BCI systems that users can easily manipulate. Recognizing the user’s intuitive imagery and translating it to the outside world is one of the critical functions of BCIs. Using the speech imagery paradigm, communication could significantly improve because it could directly convey the user’s intention through the imagined speech or conversation itself instead of through the spelling of individual letters. Moreover, intuitive decoding directly matches the interaction between user intentions and device feedback in real-world environments. Eventually, this characteristic of the intuitive paradigm could contribute to the development of practical BCI systems that provide a high degree of freedom to the user. Therefore, we expect this research topic to improve the performance of speech imagery decoding, thus enabling direct communication tools or control of external environments via internal speech.
We welcome Original Research and Review articles covering topics of interest including but not limited to:
• Speech imagery decoding (e.g., vowels, consonants, phonemes, syllables, words, and even sentences) using various brain signal modalities.
• Analysis of neural activity during overt speech or covert speech
• Design of the intuitive brain-computer interface (BCI) paradigm
• Practical communication via multimodality signals (e.g., a combination of the bio- and brain signals, EMG, EEG, etc.)
• Advanced signal processing for high-level real-time communication
• Design of BCI applications for neurorehabilitation, neurofeedback, games, mental status, and so on
Here we present the Frontiers in Human Neuroscience ‘Neural Decoding for Direct Communication in Brain-Computer Interface’ article collection. People hope for a future in which a brain-computer interface (BCI) decodes what one intuitively imagines and outputs it to the real-world environment. Once the imagined word or conversation is decoded by the BCI system, it can then be used as a neural command to control external devices (e.g., computers, robots, IoT systems., etc.). Recently, to implement these types of BCI, relevant features for the direct communication paradigm have been investigated, aiming to improve the effectiveness of capturing speech-related brain activity. With the aim of decoding intuitive speech, BCI is evolving in conjunction with significant challenges.
Speech imagery can be a key paradigm for developing intuitive BCI systems that users can easily manipulate. Recognizing the user’s intuitive imagery and translating it to the outside world is one of the critical functions of BCIs. Using the speech imagery paradigm, communication could significantly improve because it could directly convey the user’s intention through the imagined speech or conversation itself instead of through the spelling of individual letters. Moreover, intuitive decoding directly matches the interaction between user intentions and device feedback in real-world environments. Eventually, this characteristic of the intuitive paradigm could contribute to the development of practical BCI systems that provide a high degree of freedom to the user. Therefore, we expect this research topic to improve the performance of speech imagery decoding, thus enabling direct communication tools or control of external environments via internal speech.
We welcome Original Research and Review articles covering topics of interest including but not limited to:
• Speech imagery decoding (e.g., vowels, consonants, phonemes, syllables, words, and even sentences) using various brain signal modalities.
• Analysis of neural activity during overt speech or covert speech
• Design of the intuitive brain-computer interface (BCI) paradigm
• Practical communication via multimodality signals (e.g., a combination of the bio- and brain signals, EMG, EEG, etc.)
• Advanced signal processing for high-level real-time communication
• Design of BCI applications for neurorehabilitation, neurofeedback, games, mental status, and so on
