IEEE Transactions on Electron Devices · 2017 · 22 citations · 17 references
Optical MaterialsMedical ElectronicsEngineeringOptoelectronic DevicesBiomedical EngineeringOptogeneticsNeurochipSocial SciencesBiosensing SystemsBiomedical DevicesElectrode ArrayBiophysicsBio-electronic InterfacesElectrical StimulationOptoelectronic MaterialsBiophotonicsFlexible Polyimide SubstrateNeural InterfaceNeural InterfacesBrain-computer InterfaceElectro-optics DeviceBiomedical SensorsNeuroengineeringFlexible ElectronicsBiomedical DiagnosticsBioelectronicsNeuroscienceBrain ElectrophysiologyElectrophysiologyBiomemsOptoelectronicsOptical Devices
As an advanced brain-computer interface, the flexible surface electrode array has been used for spatiotemporal localization of neural interactions by recording electrocorticography (ECoG) signals over brain cortical areas. Compared with the electrical stimulation, optogenetics provides a potentially ideal way to stimulate the genetically modified brain tissue by light. In this paper, we developed an optoelectric neural interface combining a micro ECoG (μECoG) recording electrode array and a microlight-emitting diode (μLED) array. Three μLED chips were connected to a flexible polyimide substrate by a unique wire bonding method, and their light-emitting surfaces were downward and in the same plane with the substrate's lower surface, which allowed blue light directly going through the aligned holes on substrate with barely no loss. In addition, the recording electrodes were modified with electroplated platinum black or activated iridium oxide, and their stability was proved well after repetitive pressures. Mechanical strength and conformality of two μECoG arrays with 5 and 10 μm thicknesses were tested. Finally, this bidirectional neural interface was proved to be effective by an acute in vivo experiment performed by attaching two devices with varied thicknesses to the cortical surface of a mouse expressing Channelrhodopsin-2.
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A brain–computer interface using electrocorticographic signals in humans
Eric C. Leuthardt, Gerwin Schalk, Jonathan R. Wolpaw et al. · Journal of Neural Engineering · 2004 · 1.2K citations