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What is the Brain-Machine Interface

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Interfaces to monitor neural activity typically record electrical signals using electrodes in close proximity to neural tissue (eg. using MEA’s, ECoG, EEG, etc). Emerging methods aim to provide further insight by additionally observing the chemical neural activity. Current research is both streamlining traditional interfaces for the central and peripheral nervous systems (CNS and PNS) and also investigating alternative methods.


In the same way that electrical activity can be recorded within the CNS and PNS, this can also be modulated through electrical neural stimulation (ENS). Our research involves developing ENS technology to improve the efficiency and effectiveness of such interfaces. This includes electronics for efficient stimulus generation, stimulation profiles to maximise charge delivery efficiency and electrode lifetime, and strategies for the selective modulation of neuronal pathways, i.e. increasing the spatial resolution.


Recording systems capture multiple resolutions of neurological function, extracting single to multiple neuronal clusters. To understand the neurological mechanisms and translate the signals to a diagnosis, early warning or feedback to closed-loop stimulation strategy we employ a variety of signal processing methods. These include computationally efficient algorithms that can deal with non-linear and non-stationary time series data, extracting features or signal dynamics of EEG data and for single neuron clusters, to detect and classify them from their neighbours. These algorithms, we design to be computationally and hardware efficient. For this reearhers investigate different forms of data representation, signal compression and hardware implementation strategies.


Neural systems rely on wireless means for communication including both through-air (for wearable devices) and transcutaneous transmission (for implanted devices). Research in this area is investigating methods for achieving: (1) high bandwidth transcutaneous communication (UWB and optical), and (2) high efficiency power transfer (inductive and ultrasonic).

What is the Brain-Computer Interface Technology

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