224-Gbit/s 4-PAM operation of a high-modulation-bandwidth high-output-power Hi-FIT AXEL transmitter.

We fabricated an optical transmitter with high frequency and integrated design based on the flip-chip interconnection technique (Hi-FIT) and assisted extended reach electroadsorption modulator integrated distributed feedback (EADFB) laser (AXEL) for 200-Gbit/s/λ application. The Hi-FIT makes it possible to increase modulation bandwidth thanks to wire-free interconnection and peaking control techniques while the AXEL can increase the optical modulation output power thanks to an integrated semiconductor optical amplifier (SOA). The fabricated Hi-FIT AXEL transmitter has a 3-dB bandwidth of more than 66 GHz. We obtained clear 224-Gbit/s 4-level pulse amplitude modulation (4-PAM) eye diagrams with a chip-output optical modulation amplitude (OMA) of more than +7.9 dBm at distributed feedback (DFB) laser and SOA currents of 70 and 30 mA, respectively.

Mapping ECoG channel contributions to trajectory and muscle activity prediction in human sensorimotor cortex.

Studies on brain-machine interface techniques have shown that electrocorticography (ECoG) is an effective modality for predicting limb trajectories and muscle activity in humans. Motor control studies have also identified distributions of "extrinsic-like" and "intrinsic-like" neurons in the premotor (PM) and primary motor (M1) cortices. Here, we investigated whether trajectories and muscle activity predicted from ECoG were obtained based on signals derived from extrinsic-like or intrinsic-like neurons. Three participants carried objects of three different masses along the same counterclockwise path on a table. Trajectories of the object and upper arm muscle activity were predicted using a sparse linear regression. Weight matrices for the predictors were then compared to determine if the ECoG channels contributed more information about trajectory or muscle activity. We found that channels over both PM and M1 contributed highly to trajectory prediction, while a channel over M1 was the highest contributor for muscle activity prediction.

Planar n-SI-n heterostructure athermal InP (110) optical modulator.

We report an athermal InP (110) optical modulator with a simple planar n-SI-n heterostructure. A symmetrical push-pull operation provides a high-extinction ratio of >25 dB over the entire C-band and zero-chirp modulation. A Mach-Zehnder optical modulator (MZM) exhibits a 3 dB-EO bandwidth of 30 GHz and 40-Gb/s NRZ high-speed modulation with wavelength and temperature insensitive operation. We also successfully demonstrate an MZM integrated twin-IQ modulator that exhibits 56-Gb/s × 2 athermal QPSK modulation at constant drive and bias voltages.

Decoding fingertip trajectory from electrocorticographic signals in humans.

Seeking to apply brain-machine interface technology in neuroprosthetics, a number of methods for predicting trajectory of the elbow and wrist have been proposed and have shown remarkable results. Recently, the prediction of hand trajectory and classification of hand gestures or grasping types have attracted considerable attention. However, trajectory prediction for precise finger motion has remained a challenge. We proposed a method for the prediction of fingertip motions from electrocorticographic signals in human cortex. A patient performed extension/flexion tasks with three fingers. Average Pearson's correlation coefficients and normalized root-mean-square errors between decoded and actual trajectories were 0.83-0.90 and 0.24-0.48, respectively. To confirm generalizability to other users, we applied our method to the BCI Competition IV open data sets. Our method showed that the prediction accuracy of fingertip trajectory could be equivalent to that of other results in the competition.

Decoding grasp force profile from electrocorticography signals in non-human primate sensorimotor cortex.

The relatively low invasiveness of electrocorticography (ECoG) has made it a promising candidate for the development of practical, high-performance neural prosthetics. Recent ECoG-based studies have shown success in decoding hand and finger movements and muscle activity in reaching and grasping tasks. However, decoding of force profiles is still lacking. Here, we demonstrate that lateral grasp force profile can be decoded using a sparse linear regression from 15 and 16 channel ECoG signals recorded from sensorimotor cortex in two non-human primates. The best average correlation coefficients of prediction after 10-fold cross validation were 0.82±0.09 and 0.79±0.15 for our monkeys A and B, respectively. These results show that grasp force profile was successfully decoded from ECoG signals in reaching and grasping tasks and may potentially contribute to the development of more natural control methods for grasping in neural prosthetics.

Prediction of three-dimensional arm trajectories based on ECoG signals recorded from human sensorimotor cortex.

Brain-machine interface techniques have been applied in a number of studies to control neuromotor prostheses and for neurorehabilitation in the hopes of providing a means to restore lost motor function. Electrocorticography (ECoG) has seen recent use in this regard because it offers a higher spatiotemporal resolution than non-invasive EEG and is less invasive than intracortical microelectrodes. Although several studies have already succeeded in the inference of computer cursor trajectories and finger flexions using human ECoG signals, precise three-dimensional (3D) trajectory reconstruction for a human limb from ECoG has not yet been achieved. In this study, we predicted 3D arm trajectories in time series from ECoG signals in humans using a novel preprocessing method and a sparse linear regression. Average Pearson's correlation coefficients and normalized root-mean-square errors between predicted and actual trajectories were 0.44~0.73 and 0.18~0.42, respectively, confirming the feasibility of predicting 3D arm trajectories from ECoG. We foresee this method contributing to future advancements in neuroprosthesis and neurorehabilitation technology.

Prediction of hand trajectory from electrocorticography signals in primary motor cortex.

Due to their potential as a control modality in brain-machine interfaces, electrocorticography (ECoG) has received much focus in recent years. Studies using ECoG have come out with success in such endeavors as classification of arm movements and natural grasp types, regression of arm trajectories in two and three dimensions, estimation of muscle activity time series and so on. However, there still remains considerable work to be done before a high performance ECoG-based neural prosthetic can be realized. In this study, we proposed an algorithm to decode hand trajectory from 15 and 32 channel ECoG signals recorded from primary motor cortex (M1) in two primates. To determine the most effective areas for prediction, we applied two electrode selection methods, one based on position relative to the central sulcus (CS) and another based on the electrodes' individual prediction performance. The best coefficients of determination for decoding hand trajectory in the two monkeys were 0.4815 ± 0.0167 and 0.7780 ± 0.0164. Performance results from individual ECoG electrodes showed that those with higher performance were concentrated at the lateral areas and areas close to the CS. The results of prediction according with different numbers of electrodes based on proposed methods were also shown and discussed. These results also suggest that superior decoding performance can be achieved from a group of effective ECoG signals rather than an entire ECoG array.

Silica-based, compact and variable-optical-attenuator integrated coherent receiver with stable optoelectronic coupling system.

We demonstrate a compact and variable-optical-attenuator (VOA) integrated coherent receiver with a silica-based planar lightwave circuit (PLC). To realize the compact receiver, we integrate a VOA in a single PLC chip with polarization beam splitters and optical 90-degree hybrids, and employ a stable optoelectronic coupling system consisting of micro lens arrays and photodiode (PD) subcarriers with high-speed right-angled signal lines. We integrate a VOA and a coherent receiver in a 27x40x6 mm package, and successfully demodulate a 128-Gbit/s polarization division multiplexed (PDM) quadrature phase shift keying (QPSK) signal with a VOA-assisted wide dynamic range of more than 30 dB.