A Bidirectional Neural Interface IC With Chopper Stabilized BioADC Array and Charge Balanced Stimulator.

We present a bidirectional neural interface with a 4-channel biopotential analog-to-digital converter (bioADC) and a 4-channel current-mode stimulator in 180 nm CMOS. The bioADC directly transduces microvolt biopotentials into a digital representation without a voltage-amplification stage. Each bioADC channel comprises a continuous-time first-order ∆Σ modulator with a chopper-stabilized OTA input and current feedback, followed by a second-order comb-filter decimator with programmable oversampling ratio. Each stimulator channel contains two independent digital-to-analog converters for anodic and cathodic current generation. A shared calibration circuit matches the amplitude of the anodic and cathodic currents for charge balancing. Powered from a 1.5 V supply, the analog and digital circuits in each recording channel draw on average [Formula: see text] and [Formula: see text] of supply current, respectively. The bioADCs achieve an SNR of [Formula: see text] and a SFDR of [Formula: see text] , for better than 9-b ENOB. Intracranial EEG recordings from an anesthetized rat are shown and compared to simultaneous recordings from a commercial reference system to validate performance in-vivo . Additionally, we demonstrate bidirectional operation by recording cardiac modulation induced through vagus nerve stimulation, and closed-loop control of cardiac rhythm. The micropower operation, direct digital readout, and integration of electrical stimulation circuits make this interface ideally suited for closed-loop neuromodulation applications.

Principal components of hand kinematics and neurophysiological signals in motor cortex during reach to grasp movements.

A few kinematic synergies identified by principal component analysis (PCA) account for most of the variance in the coordinated joint rotations of the fingers and wrist used for a wide variety of hand movements. To examine the possibility that motor cortex might control the hand through such synergies, we collected simultaneous kinematic and neurophysiological data from monkeys performing a reach-to-grasp task. We used PCA, jPCA and isomap to extract kinematic synergies from 18 joint angles in the fingers and wrist and analyzed the relationships of both single-unit and multiunit spike recordings, as well as local field potentials (LFPs), to these synergies. For most spike recordings, the maximal absolute cross-correlations of firing rates were somewhat stronger with an individual joint angle than with any principal component (PC), any jPC or any isomap dimension. In decoding analyses, where spikes and LFP power in the 100- to 170-Hz band each provided better decoding than other LFP-based signals, the first PC was decoded as well as the best decoded joint angle. But the remaining PCs and jPCs were predicted with lower accuracy than individual joint angles. Although PCs, jPCs or isomap dimensions might provide a more parsimonious description of kinematics, our findings indicate that the kinematic synergies identified with these techniques are not represented in motor cortex more strongly than the original joint angles. We suggest that the motor cortex might act to sculpt the synergies generated by subcortical centers, superimposing an ability to individuate finger movements and adapt the hand to grasp a wide variety of objects.

State-based decoding of hand and finger kinematics using neuronal ensemble and LFP activity during dexterous reach-to-grasp movements.

The performance of brain-machine interfaces (BMIs) that continuously control upper limb neuroprostheses may benefit from distinguishing periods of posture and movement so as to prevent inappropriate movement of the prosthesis. Few studies, however, have investigated how decoding behavioral states and detecting the transitions between posture and movement could be used autonomously to trigger a kinematic decoder. We recorded simultaneous neuronal ensemble and local field potential (LFP) activity from microelectrode arrays in primary motor cortex (M1) and dorsal (PMd) and ventral (PMv) premotor areas of two male rhesus monkeys performing a center-out reach-and-grasp task, while upper limb kinematics were tracked with a motion capture system with markers on the dorsal aspect of the forearm, hand, and fingers. A state decoder was trained to distinguish four behavioral states (baseline, reaction, movement, hold), while a kinematic decoder was trained to continuously decode hand end point position and 18 joint angles of the wrist and fingers. LFP amplitude most accurately predicted transition into the reaction (62%) and movement (73%) states, while spikes most accurately decoded arm, hand, and finger kinematics during movement. Using an LFP-based state decoder to trigger a spike-based kinematic decoder [r = 0.72, root mean squared error (RMSE) = 0.15] significantly improved decoding of reach-to-grasp movements from baseline to final hold, compared with either a spike-based state decoder combined with a spike-based kinematic decoder (r = 0.70, RMSE = 0.17) or a spike-based kinematic decoder alone (r = 0.67, RMSE = 0.17). Combining LFP-based state decoding with spike-based kinematic decoding may be a valuable step toward the realization of BMI control of a multifingered neuroprosthesis performing dexterous manipulation.

Toward electrocorticographic control of a dexterous upper limb prosthesis: building brain-machine interfaces.

One of the most exciting and compelling areas of research and development is building brain machine interfaces (BMIs) for controlling prosthetic limbs. Prosthetic limb technology is advancing rapidly, and the modular prosthetic limb (MPL) of the Johns Hopkins University/ Applied Physics Laboratory (JHU/APL) permits actuation with 17 degrees of freedom in 26 articulating joints. There are many signals from the brain that can be leveraged, including the spiking rates of neurons in the cortex, electrocorticographic (ECoG) signals from the surface of the cortex, and electroencephalographic (EEG) signals from the scalp. Unlike microelectrodes that record spikes, ECoG does not penetrate the cortex and has a higher spatial specificity, signal-to-noise ratio, and bandwidth than EEG signals. We have implemented an ECoG-based system for controlling the MPL in the Johns Hopkins Hospital Epilepsy Monitoring Unit, where patients are implanted with ECoG electrode grids for clinical seizure mapping and asked to perform various recorded finger or grasp movements. We have shown that low-frequency local motor potentials (LMPs) and ECoG power in the high gamma frequency (70,150 Hz) range correlate well with grasping parameters, and they stand out as good candidate features for closed-loop control of the MPL.

Spatiotemporal variation of multiple neurophysiological signals in the primary motor cortex during dexterous reach-to-grasp movements.

To examine the spatiotemporal distribution of discriminable information about reach-to-grasp movements in the primary motor cortex upper extremity representation, we implanted four microelectrode arrays in the anterior bank and lip of the central sulcus in each of two monkeys. We used linear discriminant analysis to compare information, quantified as decoding accuracy, contained in various neurophysiological signals. For all signal types, decoding accuracy increased immediately after the movement cue, peaked around movement onset, and declined during the static hold. Spike recordings and local field potential (LFP) time domain amplitude provided more discriminable information than LFP frequency domain power. Discriminable information on movement type was distributed evenly across recording sites by LFP amplitude and 1-4 Hz power but unevenly by 100-170 Hz power and spike recordings. These latter two signal types provided higher decoding accuracies closer to the hemispheric surface than deep in the anterior bank and also provided accuracies that varied along the central sulcus. This variation in the distribution of movement-type information may be related to differences in the rostral versus caudal regions of the primary motor cortex and to its underlying somatotopic organization. The even distribution of information by LFP amplitude and 1-4 Hz power compared with the more localized distribution by 100-170 Hz power and spikes suggest that these different neurophysiological signals reflect different underlying processes that distribute information through the motor cortex during reach-to-grasp movements.

Asynchronous decoding of grasp aperture from human ECoG during a reach-to-grasp task.

Recent studies in primate neurophysiology have focused on decoding multi-joint kinematics from single unit and local field potential recordings. However, the extent to which these results can be generalized to human subjects is not known. We have recorded simultaneous electrocorticographic (ECoG) and hand kinematics in a human subject performing reach-grasp-hold of objects varying in shape and size. All Spectral features in various gamma bands (30-50 Hz, 70-100 Hz and 100-150 Hz frequency bands) were able to predict the time course of grasp aperture with high correlation (max r = 0.80) using as few as one ECoG feature from a single electrode (max r for single feature = 0.75) in single trials without prior knowledge of task timing. These results suggest that the population activity captured with ECoG contains information about coordinated finger movements that potentially can be exploited to control advanced upper limb neuroprosthetics.

Spectral modulation of LFP activity in M1 during dexterous finger movements.

Recent studies have shown that cortical local field potentials (LFP) contain information about planning or executing hand movement. While earlier research has looked at gross motor movements, we investigate the spectral modulation of LFP activity and its dependence on recording location during dexterous motor actions. In this study, we recorded LFP activity from the primary motor cortex of a primate as it performed a fine finger manipulation task involving different switches. The event-related spectral perturbations (ERSP) in four different frequency bands were considered for the analysis; 4 Hz, 6-15 Hz, 17-40 Hz and 75-170 Hz. LFPs recorded from electrodes in the hand area showed the largest change in ERSP for the highest frequency band (75-170 Hz) (p 0.05), while LFPs recorded from electrodes placed more medially in the arm area showed the largest change in ERSP for the lowest frequency band (4 Hz) (p 0.05). Furthermore, the spectral information from the <4 Hz and 75-150 Hz frequency bands was used to successfully decode the three dexterous grasp movements with an average accuracy of up to 81%. Although previous research has shown that multi-unit neuronal activity can be used to decode fine motor movements, these results demonstrate that LFP activity can also be used to decode dexterous motor tasks. This has implications for future neuroprosthetic devices due to the robustness of LFP signals for chronic recording.

From spikes to EEG: integrated multichannel and selective acquisition of neuropotentials.

Electrical signals recorded from the brain cover a wide range of amplitudes, frequencies, and spatial scales, from spikes and local field potentials (LFP) inside the brain to electrocorticograms (ECoG) and electroencepalograms (EEG) outside. Each of these signal modalities represent different aspects of neural dynamics that can be combined to infer brain state and function in a broader context.We present a 16-channel interface circuit fabricated in a 0.5 mum CMOS process for the selective acquisition and digitization of any of the modalities. Each channel features a fixed gain bandpass amplifier with a tunable frequency response which allows isolation of the signal of interest without hardware modification and a programmable gain/resolution analog to digital converter (ADC). The bandpass amplifier analog front end has an input referred noise of 1.94 microV(rms) for a bandwidth of 8.2 kHz while drawing 12.2 microA of current from a 3.3 V supply. Experimental recordings with the system show spike signals in rat somatosensory cortex as well as alpha EEG activity in a human subject.