BioCNN: A Hardware Inference Engine for EEG-based Emotion Detection

• Published in: IEEE access Vol. 8; p. 1
• Main Authors: Gonzalez, Hector A., Muzaffar, Shahzad, Yoo, Jerald, Elfadel, Ibrahim M.
• Format: Journal Article
• Language: English
• Published: Piscataway IEEE 01.01.2020; The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
• Subjects: Accelerators; Artificial neural networks; Classification; Classifiers; Convolutional Neural Networks; Datasets; Edge AI; EEG; Electroencephalography; Emotion Recognition; Emotions; Engines; Feature extraction; FPGA; Hardware; Hardware Accelerator; Hardware Parallelism; Human relations; Inference; Low cost; Machine learning; Neurological diseases; Noise reduction; Pipelining; Real-time systems; Software; Training; Visual stimuli; Wearable technology
• ISSN: 2169-3536; 2169-3536
• DOI: 10.1109/ACCESS.2020.3012900

EEG-based emotion classifiers have the potential of significantly improving the social integration of patients suffering from neurological disorders such as Amyotrophic Lateral Sclerosis or the acute stages of Alzheimer's disease. Emotion classifiers have historically used software on general-purpose computers and operating under off-line conditions. Yet the wearability of such classifiers is a must if they are to enable the socialization of critical-care patients. Such wearability requires the use of low-power hardware accelerators that would enable near real-time classification and extended periods of operations. In this paper, we architect, design, implement, and test a handcrafted, hardware Convolutional Neural Network, named BioCNN, optimized for EEG-based emotion detection and other bio-medical applications. The EEG signals are generated using a low-cost, off-the-shelf device, namely, Emotiv Epoc+, and then denoised and pre-processed ahead of their use by BioCNN. For training and testing, BioCNN uses three repositories of emotion classification datasets, including the publicly available DEAP and DREAMER datasets, along with an original dataset collected in-house from 5 healthy subjects using standard visual stimuli. A subject-specific training approach is used under TensorFlow to train BioCNN, which is implemented using the Digilent Atlys Board with a low-cost Spartan-6 FPGA. The experimental results show a competitive energy efficiency of 11 GOps/W, a throughput of 1.65 GOps that is in line with the real-time specification of a wearable device, and a latency of less than 1 ms, which is smaller than the 150 ms required for human interaction times. Its emotion inference accuracy is competitive with the top software-based emotion detectors.