Decoding EEG and LFP signals using deep learning: heading TrueNorth

@article{Nurse2016DecodingEA,
  title={Decoding EEG and LFP signals using deep learning: heading TrueNorth},
  author={Ewan S. Nurse and Benjamin Scott Mashford and Antonio Jimeno-Yepes and Isabell Kiral-Kornek and Stefan Harrer and Dean R. Freestone},
  journal={Proceedings of the ACM International Conference on Computing Frontiers},
  year={2016}
}
Deep learning technology is uniquely suited to analyse neurophysiological signals such as the electroencephalogram (EEG) and local field potentials (LFP) and promises to outperform traditional machine-learning based classification and feature extraction algorithms. Furthermore, novel cognitive computing platforms such as IBM's recently introduced neuromorphic TrueNorth chip allow for deploying deep learning techniques in an ultra-low power environment with a minimum device footprint. Merging… Expand
Targeting EEG/LFP Synchrony with Neural Nets
TLDR
A Gaussian process adapter is developed to combine analysis over distinct electrode layouts, allowing the joint processing of multiple datasets to address overfitting and improve generalizability and it is demonstrated that the proposed framework effectively tracks neural dynamics on children in a clinical trial on Autism Spectrum Disorder. Expand
Decoding EEG Brain Signals using Recurrent Neural Networks
Brain-computer interfaces (BCIs) based on electroencephalography (EEG) enable direct communication between humans and computers by analyzing brain activity. Specifically, modern BCIs are capable ofExpand
Overview of Deep Learning Architectures for Classifying Brain Signals
One of the challenges in modeling cognitive events from electroencephalogram (EEG) data is finding representations that are invariant to interand intra-subject differences, as well as the inherentExpand
TrueNorth-enabled real-time classification of EEG data for brain-computer interfacing
TLDR
The results on a EEG-based hand squeeze task show that using a convolutional neural network and a time preserving signal representation strategy provides a good balance between high accuracy and feasibility in a real-time application. Expand
Deep learning-based electroencephalography analysis: a systematic review
TLDR
This work reviews 156 papers that apply DL to EEG, published between January 2010 and July 2018, and spanning different application domains such as epilepsy, sleep, brain-computer interfacing, and cognitive and affective monitoring, to extract trends and highlight interesting approaches in order to inform future research and formulate recommendations. Expand
Brain activity recognition with a wearable fNIRS using neural networks
TLDR
Evaluating the performance of deep neural networks and convolutional neural networks in the classification of different stimulus in the prefrontal cortex, caused by predefined activities: subtractions, word generation, and rest shows that neither type of network is able to distinguish subtraction from word generation. Expand
A Survey on Deep Learning based Brain Computer Interface: Recent Advances and New Frontiers
TLDR
This article systematically investigates brain signal types for BCI and related deep learning concepts for brain signal analysis, and presents a comprehensive survey of deep learning techniques used forBCI. Expand
Deep learning approaches for neural decoding: from CNNs to LSTMs and spikes to fMRI
TLDR
The architectures used for extracting useful features from neural recording modalities ranging from spikes to EEG are described, and how deep learning has been leveraged to predict common outputs including movement, speech, and vision is explored. Expand
A Robust Low-Cost EEG Motor Imagery-Based Brain-Computer Interface
TLDR
It is shown that the method outperforms previous OpenBCI MI-based BCIs, thereby extending the BCI capabilities of this low-cost device and leveraged neurofeedback, deep learning, and wider temporal windows to improve performance. Expand
Applying Transfer Learning To Deep Learned Models For EEG Analysis
TLDR
This project proposes a method that can produce reliable electroencephalography (EEG) signal classification, based on modest amounts of training data through the use of transfer learning, and explores transferability of knowledge between trained models over different experiments, called inter-experimental transfer learning. Expand
...
1
2
3
4
5
...

References

SHOWING 1-10 OF 39 REFERENCES
Modeling electroencephalography waveforms with semi-supervised deep belief nets: fast classification and anomaly measurement.
TLDR
It is demonstrated how the unsupervised step of DBN learning produces an autoencoder that can naturally be used in anomaly measurement, and results indicate that DBNs and raw data inputs may be more effective for online automated EEG waveform recognition than other common techniques. Expand
A Generalizable Brain-Computer Interface (BCI) Using Machine Learning for Feature Discovery
TLDR
A generalized method for classifying motor-related neural signals for a brain-computer interface (BCI) based on a stochastic machine learning method, which has been shown to give accurate results across different motor tasks and signal types as well as between subjects. Expand
Deep Feature Learning Using Target Priors with Applications in ECoG Signal Decoding for BCI
TLDR
This work describes a weakly supervised learning method of a prior supervised convolutional stacked auto-encoders (PCSA), of which information in the target variables is represented probabilistically using a Gaussian Bernoulli restricted Boltzmann machine (RBM). Expand
Backpropagation for Energy-Efficient Neuromorphic Computing
TLDR
This work treats spikes and discrete synapses as continuous probabilities, which allows training the network using standard backpropagation and naturally maps to neuromorphic hardware by sampling the probabilities to create one or more networks, which are merged using ensemble averaging. Expand
Speech reconstruction from human auditory cortex with deep neural networks
TLDR
The efficacy of deep neural network models in decoding speech signals from neural responses and how changing the number of hidden layers in the network affects the reconstruction accuracy are revealed and a method for improving the performance of brain computer interfaces with prosthetic applications is provided. Expand
Individual finger control of a modular prosthetic limb using high-density electrocorticography in a human subject.
TLDR
The results demonstrate the ability of ECoG-based BMIs to leverage the native functional anatomy of sensorimotor cortical populations to immediately control individual finger movements in real time. Expand
Neuronal ensemble control of prosthetic devices by a human with tetraplegia
TLDR
Initial results for a tetraplegic human using a pilot NMP suggest that NMPs based upon intracortical neuronal ensemble spiking activity could provide a valuable new neurotechnology to restore independence for humans with paralysis. Expand
Seizure Prediction: Science Fiction or Soon to Become Reality?
TLDR
It is argued that seizure prediction is possible; however, most previous attempts have used data with an insufficient amount of information to solve the problem, so developments in obtaining long-term data that enables better characterisation of signal features and trends are discussed. Expand
An Electrocorticographic Brain Interface in an Individual with Tetraplegia
TLDR
It is demonstrated that ECoG signals recorded from the sensorimotor cortex can be used for real-time device control in paralyzed individuals. Expand
Brain-Computer Interfaces: Principles and Practice
Contributors PART I: INTRODUCTION 1. Brain-Computer Interfaces: Something New under the Sun Jonathan R. Wolpaw and Elizabeth Winter Wolpaw PART II: BRAIN SIGNALS FOR BCIs 2. Neuronal Activity inExpand
...
1
2
3
4
...