Abed Khorasani

In this paper a novel automated and unsupervised method for removing artifacts from multichannel field potential signals is introduced which can be used in brain computer interface (BCI) applications. The method, which is called minimum noise estimate (MNE) filter is based on an iterative thresholding followed by Rayleigh quotient which tries to find an estimate of the noise and to minimize it over the original signal. MNE filter is capable to operate without any prior information about field potential signals. The performance of the proposed method is evaluated by its application on two different type of signals namely electrocorticogram (ECoG) and electroencephalogram (EEG) datasets through a decoding procedure. The results indicate that the proposed method outperforms well-known artifacts removal techniques such as common average referencing (CAR), Laplacian method, independent component analysis (ICA) and wavelet denoising approach.

The need for a miniaturized device that can perform closed-loop operation is imminent with the growing interest in brain-controlled devices and in stimulation to treat neural disorders. This work presents the Neural Closed-Loop Implantable Platform (NeuralCLIP), a modular FPGA-based device that can record neural signals, process them locally to detect an event and trigger neural stimulation based on the detection. Specifically, the NeuralCLIP is designed to record and process different neural signals in the frequency range between 20 Hz and 1 kHz. It is a flexible platform that can be reconfigured to optimize parameters like channel count and operation frequency based on the processing requirements. The signal-agnostic feature is demonstrated by testing the device with calibration signals from standard bio-signal emulators. The application focus for this device is a brain-computer-spinal interface (BCSI) which is demonstrated based on local field potential (LFP) signals recorded fro...

Artifact removal is a key step toward designing real-world and efficient brain computer interfaces. Here we describe an automatic blind source separation algorithm applicable to real-time signal processing. The algorithm combines the generalized eigenvalue decomposition technique with Laplacian filtering to separate desired and undesired subspaces, exclude artifact sources and recover artifact-free cortical signals. The algorithm outperforms commonly used artifact removal methods in brain computer interfaces as measured by cortical decoding performance.

We present a new class of carbon-based neural probes that consist of homogeneous glassy carbon (GC) microelectrodes, interconnects and bump pads. These electrodes have purely capacitive behavior with exceptionally high charge storage capacity (CSC) and are capable of sustaining more than 3.5 billion cycles of bi-phasic pulses at charge density of 0.25 mC/cm. These probes enable both high SNR (>16) electrical signal recording and remarkably high-resolution real-time neurotransmitter detection, on the same platform. Leveraging a new 2-step, double-sided pattern transfer method for GC structures, these probes allow extended long-term electrical stimulation with no electrode material corrosion. Cross-section characterization through FIB and SEM imaging demonstrate strong attachment enabled by hydroxyl and carbonyl covalent bonds between GC microstructures and top insulating and bottom substrate layers. Extensive in-vivo and in-vitro tests confirmed: (i) high SNR (>16) recordings, ...

A local field potential (LFP) signal is an alternative source to neural action potentials for decoding kinematic and kinetic information from the brain. Here, we demonstrate that the better extraction of force-related features from multichannel LFPs improves the accuracy of force decoding. We propose that applying canonical correlation analysis (CCA) filter on the envelopes of separate frequency bands (band-specific CCA) separates non-task related information from the LFPs. The decoding accuracy of the continuous force signal based on the proposed method were compared with three feature reduction methods: 1) band-specific principal component analysis (band-specific PCA) method that extract the components which leads to maximum variance from the envelopes of different frequency bands; 2) correlation coefficient-based (CC-based) feature reduction that selects the best features from the envelopes sorted based on the absolute correlation coefficient between each envelope and the target force signal; and 3) mutual information-based (MI-based) feature reduction that selects the best features from the envelopes sorted based on the mutual information between each envelope and output force signal. The band-specific CCA method outperformed band-specific PCA with 11% improvement, CC-based feature reduction with 16% improvement, and MI-based feature reduction with 18% improvement. In the online brain control experiments, the real-time decoded force signal from the 16-channel LFPs based on the proposed method was used to move a mechanical arm. Two rats performed 88 trials in seven sessions to control the mechanical arm based on the 16-channel LFPs.

In this paper a novel automated and unsupervised method for removing artifacts from multichannel field potential signals is introduced which can be used in brain computer interface (BCI) applications. The method, which is called minimum noise estimate (MNE) filter is based on an iterative thresholding followed by Rayleigh quotient which tries to find an estimate of the noise and to minimize it over the original signal. MNE filter is capable to operate without any prior information about field potential signals. The performance of the proposed method is evaluated by its application on two different type of signals namely electrocorticogram (ECoG) and electroencephalogram (EEG) datasets through a decoding procedure. The results indicate that the proposed method outperforms well-known artifacts removal techniques such as common average referencing (CAR), Laplacian method, independent component analysis (ICA) and wavelet denoising approach.