Electrode clustering and bandpass analysis of eeg data for gaze estimation

In this study, we validate the findings of previously published papers, showing the feasibility of an Electroencephalography (EEG) based gaze estimation. Moreover, we extend previous research by demonstrating that with only a slight drop in model performance, we can significantly reduce the number of electrodes, indicating that a high-density, expensive EEG cap is not necessary for the purposes of EEG-based eye tracking. Using data-driven approaches, we establish which electrode clusters impact gaze estimation and how the different types of EEG data preprocessing affect the models’ performance. Finally, we also inspect which recorded frequencies are most important for the defined tasks

Investigation of the input-output relationship of engineered neural networks using high-density microelectrode arrays

Bottom-up neuroscience utilizes small, engineered biological neural networks to study neuronal activity in systems of reduced complexity. We present a platform that establishes up to six independent networks formed by primary rat neurons on planar complementary metal–oxide–semiconductor (CMOS) microelectrode arrays (MEAs). We introduce an approach that allows repetitive stimulation and recording of network activity at any of the over 700 electrodes underlying a network. We demonstrate that the continuous application of a repetitive super-threshold stimulus yields a reproducible network answer within a 15 ms post-stimulus window. This response can be tracked with high spatiotemporal resolution across the whole extent of the network. Moreover, we show that the location of the stimulation plays a significant role in the networks' early response to the stimulus. By applying a stimulation pattern to all network-underlying electrodes in sequence, the sensitivity of the whole network to the stimulus can be visualized. We demonstrate that microchannels reduce the voltage stimulation threshold and induce the strongest network response. By varying the stimulation amplitude and frequency we reveal discrete network transition points. Finally, we introduce vector fields to follow stimulation-induced spike propagation pathways within the network. Overall we show that our defined neural networks on CMOS MEAs enable us to elicit highly reproducible activity patterns that can be precisely modulated by stimulation amplitude, stimulation frequency and the site of stimulation.Peer reviewe

Engineered Biological Neural Networks on High Density CMOS Microelectrode Arrays

In bottom-up neuroscience, questions on neural information processing are addressed by engineering small but reproducible biological neural networks of defined network topology in vitro. The network topology can be controlled by culturing neurons within polydimethylsiloxane (PDMS) microstructures that are combined with microelectrode arrays (MEAs) for electric access to the network. However, currently used glass MEAs are limited to 256 electrodes and pose a limitation to the spatial resolution as well as the design of more complex microstructures. The use of high density complementary metal-oxide-semiconductor (CMOS) MEAs greatly increases the spatial resolution, enabling sub-cellular readout and stimulation of neurons in defined neural networks. Unfortunately, the non-planar surface of CMOS MEAs complicates the attachment of PDMS microstructures. To overcome the problem of axons escaping the microstructures through the ridges of the CMOS MEA, we stamp-transferred a thin film of hexane-diluted PDMS onto the array such that the PDMS filled the ridges at the contact surface of the microstructures without clogging the axon guidance channels. This method resulted in 23 % of structurally fully connected but sealed networks on the CMOS MEA of which about 45 % showed spiking activity in all channels. Moreover, we provide an impedance-based method to visualize the exact location of the microstructures on the MEA and show that our method can confine axonal growth within the PDMS microstructures. Finally, the high spatial resolution of the CMOS MEA enabled us to show that action potentials follow the unidirectional topology of our circular multi-node microstructure.ISSN:1662-453XISSN:1662-454

Investigation of the input-output relationship of engineered neural networks using high-density microelectrode arrays

Bottom-up neuroscience utilizes small, engineered biological neural networks to study neuronal activity in systems of reduced complexity. We present a platform that establishes up to six independent networks formed by primary rat neurons on planar complementary metal–oxide–semiconductor (CMOS) microelectrode arrays (MEAs). We introduce an approach that allows repetitive stimulation and recording of network activity at any of the over 700 electrodes underlying a network. We demonstrate that the continuous application of a repetitive super-threshold stimulus yields a reproducible network answer within a 15 ms post-stimulus window. This response can be tracked with high spatiotemporal resolution across the whole extent of the network. Moreover, we show that the location of the stimulation plays a significant role in the networks' early response to the stimulus. By applying a stimulation pattern to all network-underlying electrodes in sequence, the sensitivity of the whole network to the stimulus can be visualized. We demonstrate that microchannels reduce the voltage stimulation threshold and induce the strongest network response. By varying the stimulation amplitude and frequency we reveal discrete network transition points. Finally, we introduce vector fields to follow stimulation-induced spike propagation pathways within the network. Overall we show that our defined neural networks on CMOS MEAs enable us to elicit highly reproducible activity patterns that can be precisely modulated by stimulation amplitude, stimulation frequency and the site of stimulation.ISSN:0956-5663ISSN:1873-423