An FPGA-Embedded Brain-Computer Interface System to Support Individual Autonomy in Locked-In Individuals

Abstract

Brain-computer interfaces (BCI) can detect specific EEG patterns and translate them into control signals for external devices by providing people suffering from severe motor disabilities with an alternative/additional channel to communicate and interact with the outer world. Many EEG-based BCIs rely on the P300 event-related potentials, mainly because they require training times for the user relatively short and provide higher selection speed. This paper proposes a P300-based portable embedded BCI system realized through an embedded hardware platform based on FPGA (field-programmable gate array), ensuring flexibility, reliability, and high-performance features. The system acquires EEG data during user visual stimulation and processes them in a real-time way to correctly detect and recognize the EEG features. The BCI system is designed to allow to user to perform communication and domotic controls.

Real‐time field‐programmable gate array‐based closed‐loop deep brain stimulation platform targeting cerebellar circuitry rescues motor deficits in a mouse model of cerebellar ataxia

“…Average power spectral densities, where X-axis is frequency (Hz); Y-axis is power spectral density (μV 2 of each frequency). Significant increases in theta (4-8 Hz), beta (13)(14)(15)(16)(17)(18)(19)(20), and gamma (30-80 Hz) power were detected in the (C) cerebellum and (D) motor cortex of ataxia mice. Each dot represents average power spectral density per mouse.…”

“…These EEG spectra were computed over 0-80 Hz frequency range at a resolution of 0.25 Hz (Figure 3A,B). Significant increases in theta (4-8 Hz), beta (13)(14)(15)(16)(17)(18)(19)(20), and gamma (30-80 Hz) powers in both brain areas were detected in ataxia mice, while delta (0-4 Hz) and alpha (8-13 Hz) powers were unaffected in ataxia mice with or without DBS (Figure 3C,D). These data indicate that closed-loop DCN-DBS reduced movement-related EEG oscillatory activity and restored a baseline state cerebellocortical network.…”

“…12 BCI acquires physiological signals (e.g., brain signals), analyses them to extract specific features that serves as disease biomarker, and translates them into commands that control application devices like the stimulator for closed-loop deep brain stimulation. 13 BCI has been applied across diverse neurotechnological applications, playing a significant role in the development of closed-loop DBS.…”

“…The reconfigurable FPGA integrated circuit uses fixed‐point computation to develop a hardware‐in‐the‐loop real‐time platform and implement bidirectional communications in animal experiments, such as brain‐computer interface (BCI) systems 12 . BCI acquires physiological signals (e.g., brain signals), analyses them to extract specific features that serves as disease biomarker, and translates them into commands that control application devices like the stimulator for closed‐loop deep brain stimulation 13 . BCI has been applied across diverse neurotechnological applications, playing a significant role in the development of closed‐loop DBS.…”

Domotics in inclusion and health: Descriptive Study

“…In [22] it is indicated that brain-computer interfaces are capable of detecting specific patterns and translate them into control signals for external devices by providing users suffering from serious motor-disabilities an alternative solution to communicate and interact with the world.…”