ROS-Neuro Integration of Deep Convolutional Autoencoders for EEG Signal Compression in Real-time BCIs

Autor: Raffaello Brondi, Davide Bacciu, Luca Ascari, Michele Barsotti, Andrea Valenti
Jazyk: angličtina
Rok vydání: 2020
Předmět:
Signal Processing (eess.SP)
FOS: Computer and information sciences
Computer Science - Machine Learning
Brain-Computer Interface
Computer science
Pipeline (computing)
Real-time computing
Latency (audio)
Machine Learning (stat.ML)
02 engineering and technology
010501 environmental sciences
01 natural sciences
Machine Learning (cs.LG)
Deep Learning
Statistics - Machine Learning
Encoding (memory)
0202 electrical engineering
electronic engineering
information engineering
FOS: Electrical engineering
electronic engineering
information engineering
Electrical Engineering and Systems Science - Signal Processing
ROS-Neuro
0105 earth and related environmental sciences
Jitter
business.industry
Deep learning
Signal compression
020201 artificial intelligence & image processing
Node (circuits)
Artificial intelligence
business
Zdroj: SMC
Popis: Typical EEG-based BCI applications require the computation of complex functions over the noisy EEG channels to be carried out in an efficient way. Deep learning algorithms are capable of learning flexible nonlinear functions directly from data, and their constant processing latency is perfect for their deployment into online BCI systems. However, it is crucial for the jitter of the processing system to be as low as possible, in order to avoid unpredictable behaviour that can ruin the system's overall usability. In this paper, we present a novel encoding method, based on on deep convolutional autoencoders, that is able to perform efficient compression of the raw EEG inputs. We deploy our model in a ROS-Neuro node, thus making it suitable for the integration in ROS-based BCI and robotic systems in real world scenarios. The experimental results show that our system is capable to generate meaningful compressed encoding preserving to original information contained in the raw input. They also show that the ROS-Neuro node is able to produce such encodings at a steady rate, with minimal jitter. We believe that our system can represent an important step towards the development of an effective BCI processing pipeline fully standardized in ROS-Neuro framework.
Accepted at the IEEE International Conference on Systems, Man, and Cybernetics (SMC 2020)
Databáze: OpenAIRE
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