Brain-computer interfaces are an important track for future global industries, and large-scale, high-quality EEG data is the core foundation for advancing technology from laboratories to industrialization. Yesterday afternoon, China's research team released a novel EEG signal acquisition device. For the first time in the world, thousands of people across regions can simultaneously collect EEG signals, taking a key step in the development of general technology for neural model training and brain-computer interface. According to the R&D team, achieving simultaneous collection of 1,000 people across regions has overcome two major technical difficulties: one is to ensure signal acquisition accuracy while miniaturizing equipment, and the other is to overcome the effects of network delays, achieve accurate alignment of millisecond time between multiple devices and regions, and ensure that different EEG signals can be uniformly analyzed. In the future, large-scale EEG acquisition technology will continue to deposit data for training basic neural models. This also means that AI's learning materials will no longer be limited to indirect information such as text, images, and videos, but can directly understand human cognitive states through neural signals.

Brain-computer interfaces are an important track for future global industries, and large-scale, high-quality EEG data is the core foundation for advancing technology from laboratories to industrialization. Yesterday afternoon, China's research team released a novel EEG signal acquisition device. For the first time in the world, thousands of people across regions can simultaneously collect EEG signals, taking a key step in the development of general technology for neural model training and brain-computer interface. According to the R&D team, achieving simultaneous collection of 1,000 people across regions has overcome two major technical difficulties: one is to ensure signal acquisition accuracy while miniaturizing equipment, and the other is to overcome the effects of network delays, achieve accurate alignment of millisecond time between multiple devices and regions, and ensure that different EEG signals can be uniformly analyzed. In the future, large-scale EEG acquisition technology will continue to deposit data for training basic neural models. This also means that AI's learning materials will no longer be limited to indirect information such as text, images, and videos, but can directly understand human cognitive states through neural signals.