A5037897747- EEG and Brain-Computer Interfaces
- Neuroscience and Neural Engineering
- Neural dynamics and brain function
- Advanced Memory and Neural Computing
- Functional Brain Connectivity Studies
- Neural Networks and Applications
- Blind Source Separation Techniques
- Fuzzy Logic and Control Systems
- Advanced MRI Techniques and Applications
Imperial College London
2019-2022
We present a platform technology encompassing family of innovations that together aim to tackle key challenges with existing implantable brain machine interfaces. The ENGINI (Empowering Next Generation Implantable Neural Interfaces) utilizes 3-tier network (external processor, cranial transponder, intracortical probes) inductively couple power to, and communicate data from, distributed array freely-floating mm-scale probes. Novel features integrated into each probe include: (1) an niobium...
Abstract Objective. Various on-workstation neural-spike-based brain machine interface (BMI) systems have reached the point of in-human trials, but on-node and on-implant BMI are still under exploration. Such constrained by area battery. Researchers should consider algorithm complexity, available resources, power budgets, CMOS technologies, choice platforms when designing systems. However, effect these factors is currently unclear. Approaches. Here we proposed a novel real-time 128 channel...
This study details the development of a novel, approx. £20 electroencephalogram (EEG)-based brain-computer interface (BCI) intended to offer financially and operationally accessible device that can be deployed on mass scale facilitate education public engagement in domain EEG sensing neurotechnologies. Real-time decoding steady-state visual evoked potentials (SSVEPs) is achieved using variations widely-used canonical correlation analysis (CCA) algorithm: multi-set CCA generalised CCA. All...
This paper investigates the effectiveness of four Huffman-based compression schemes for different intracortical neural signals and sample resolutions. The motivation is to find effective lossless, low-complexity data Wireless Intracortical Brain-Machine Interfaces (WI-BMI). considered include pre-trained Lone 1st 2nd order encoding [1], Delta encoding, Linear Neural Network Time (LNNT) [2]. Maximum codeword-length limited versions are also protect against overfit training data. Extracellular...
This paper investigates to what extent Long Short-Term Memory (LSTM) decoders can use Local Field Potentials (LFPs) predict Single-Unit Activity (SUA) in Macaque Primary Motor cortex. The motivation is determine degree the LFP signal be used as a proxy for SUA, both neuroscience and Brain-Computer Interface (BCI) applications. Firstly, results suggest that prediction quality varies significantly by implant location or animal. However, within each / animal, seems correlated with amount of...
Brain-machine interfaces (BMI) are tools for treating neurological disorders and motor-impairments. It is essential that the next generation of intracortical BMIs wireless so as to remove percutaneous connections, i.e. wires, associated mechanical infection risks. This required effective translation into clinical applications one remaining bottlenecks. However, due cortical tissue thermal dissipation safety limits, on-implant power consumption must be strictly limited. Therefore, both neural...
Abstract Brain-machine interfaces (BMI) are tools for treating neurological disorders and motor-impairments. It is essential that the next generation of intracortical BMIs wireless so as to remove percutaneous connections, i.e. wires, associated mechanical infection risks. This required effective translation into clinical applications one remaining bottlenecks. However, due cortical tissue thermal dissipation safety limits, on-implant power consumption must be strictly limited. Therefore,...
Abstract This paper investigates the relationship between Multi-Unit Activity (MUA) Binning Period (BP) and Brain-Computer Interface (BCI) decoding performance using Long-Short Term Memory decoders. The motivation is to determine whether lossy compression of MUA via increasing BP has any adverse consequences for BCI Behavioral Decoding Performance (BDP). Neural data originates from intracortical recordings Macaque Primary Motor cortex [1]. BDP measured by Pearson correlation r observed...
Abstract It is of great interest in neuroscience to determine what frequency bands the brain have covarying power. This would help us robustly identify signatures neural processes. However date, best author’s knowledge, a comprehensive statistical approach this question that accounts for intra-frequency autocorrelation, frequency-domain oversampling, and multiple testing under dependency has not been undertaken. As such, work presents novel significance test correlated power across broad...
This paper investigates the relationship between Multi-Unit Activity (MUA) Binning Period (BP) and Brain-Computer Interface (BCI) decoding performance using Long-Short Term Memory decoders. The motivation is to determine whether lossy compression of MUA via increasing BP has any adverse consequences for BCI Behavioral Decoding Performance (BDP). Neural data originates from intracortical recordings Macaque Primary Motor cortex. BDP measured by Pearson correlation r observed predicted velocity...
Abstract Recent years have demonstrated the feasibility of using intracortical Brain-Machine Interfaces (iBMIs), by decoding thoughts, for communication and cursor control tasks. iBMIs are increasingly becoming wireless due to risk infection mechanical failure, typically associated with percutaneous connections. The itself, however, increases power consumption further; total dissipation being strictly limited safety heating limits cortical tissue. Since is proportional bandwidth, output Bit...
Abstract It is of great interest in neuroscience to determine what frequency bands the brain contain common information. However, date, a comprehensive statistical approach this question has been lacking. As such, work presents novel significance test for correlated power across non-stationary time series. The accounts biases that often go untreated time-frequency analysis, i.e. intra-frequency autocorrelation, inter-frequency non-dyadicity, and multiple testing under dependency. used all...
Abstract This study details the development of a novel, approx. £20 electroencephalogram (EEG)-based brain-computer interface (BCI) intended to offer financially and operationally accessible device that can be deployed on mass scale facilitate education public engagement in domain EEG sensing neurotechnologies. Real-time decoding steady-state visual evoked potentials (SSVEPs) is achieved using variations widely-used canonical correlation analysis (CCA) algorithm: multi-set CCA generalised...