Mohsen Mollazadeh

Micropower CMOS Integrated Low-Noise Amplification, Filtering, and Digitization of Multimodal Neuropotentials

Electrical activity in the brain spans a wide range of spatial and temporal scales, requiring simultaneous recording of multiple modalities of neurophysiological signals in order to capture various aspects of brain state dynamics. Here, we present a 16-channel neural interface integrated circuit fabricated in a 0.5 mum 3M2P CMOS process for selective digital acquisition of biopotentials across the spectrum of neural signal modalities in the brain, ranging from single spike action potentials to local field potentials (LFP), electrocorticograms (ECoG), and electroencephalograms (EEG). Each channel is composed of a tunable bandwidth, fixed gain front-end amplifier and a programmable gain/resolution continuous-time incremental DeltaSigma analog-to-digital converter (ADC). A two-stage topology for the front-end voltage amplifier with capacitive feedback offers independent tuning of the amplifier bandpass frequency corners, and attains a noise efficiency factor (NEF) of 2.9 at 8.2 kHz bandwidth for spike recording, and a NEF of 3.2 at 140 Hz bandwidth for EEG recording. The amplifier has a measured midband gain of 39.6 dB, frequency response from 0.2 Hz to 8.2 kHz, and an input-referred noise of 1.94 muV rms while drawing 12.2 muA of current from a 3.3 V supply. The lower and higher cutoff frequencies of the bandpass filter are adjustable from 0.2 to 94 Hz and 140 Hz to 8.2 kHz, respectively. At 10-bit resolution, the ADC has an SNDR of 56 dB while consuming 76 muW power. Time-modulation feedback in the ADC offers programmable digital gain (1-4096) for auto-ranging, further improving the dynamic range and linearity of the ADC. Experimental recordings with the system show spike signals in rat somatosensory cortex as well as alpha EEG activity in a human subject.

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

In this paper, an ECoG-based system for controlling the MPL where patients were implanted with ECoG electrode grids for clinical seizure mapping and asked to perform various recorded finger or grasp movements.

A VLSI Neural Monitoring System With Ultra-Wideband Telemetry for Awake Behaving Subjects

Long term monitoring of neuronal activity in awake behaving subjects can provide fundamental information about brain dynamics for both neuroscience and neuroengineering applications. Recent advances in VLSI systems has focused on designing wireless neural recording systems which can be mounted on animals and acquire neural signals in real time. These advances provide an unparalleled opportunity to study phenomenon such as neural plasticity in both a basic science setting (learning and memory), and also a clinical setting (injury and recovery). Here we present an integrated VLSI system for wireless telemetry of the entire spectrum of neural signals, spikes, local field potentials, electrocorticograms (ECoG) and electroencephalograms (EEG). The system integrates two custom designed VLSI chips, a 16 channel neural interface which can amplify, filter and digitize neural data up to 16 kS/sec and 12 bits and a low power ultra-wideband (UWB) chip which can transmit data at rates up to 14 Mbps. The entire system which includes these VLSI circuits, a digital interface board and a battery, is small, 1.2×1.2×2.6 in3, and light weight, 33 grams, so it can be chronically mounted on a rat. The system consumes 32.8 mA at 3.3V and can record for 6 hours running from the 200 mAh coin cell battery. Bench-top and in vitro characterization of the system showed comparable performance to the wired recording system.

Wireless Micropower Instrumentation for Multimodal Acquisition of Electrical and Chemical Neural Activity

The intricate coupling between electrical and chemical activity in neural pathways of the central nervous system, and the implication of this coupling in neuropathologies, such as Parkinsons disease, motivates simultaneous monitoring of neurochemical and neuropotential signals. However, to date, neurochemical sensing has been lacking in integrated clinical instrumentation as well as in brain-computer interfaces (BCI). Here, we present an integrated system capable of continuous acquisition of data modalities in awake, behaving subjects. It features one channel each of a configurable neuropotential and a neurochemical acquisition system. The electrophysiological channel is comprised of a 40-dB gain, fully differential amplifier with tunable bandwidth from 140 Hz to 8.2 kHz. The amplifier offers input-referred noise below 2 muV rms for all bandwidth settings. The neurochemical module features a picoampere sensitivity potentiostat with a dynamic range spanning six decades from picoamperes to microamperes. Both systems have independent on-chip, configurable DeltaSigma analog-to-digital converters (ADCs) with programmable digital gain and resolution. The system was also interfaced to a wireless power harvesting and telemetry module capable of powering up the circuits, providing clocks for ADC operation, and telemetering out the data at up to 32 kb/s over 3.5 cm with a bit-error rate of less than 10-5. Characterization and experimental results from the electrophysiological and neurochemical modules as well as the full system are presented.

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.

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.

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.

A VLSI neural monitoring system with ultra-wideband telemetry for awake behaving subjects

Long-term monitoring of neuronal activity in awake behaving subjects can provide fundamental information about brain dynamics for neuroscience and neuroengineering applications. Here, we present a miniature, lightweight, and low-power recording system for monitoring neural activity in awake behaving animals. The system integrates two custom designed very-large-scale integrated chips, a neural interface module fabricated in 0.5 μm complementary metal-oxide semiconductor technology and an ultra-wideband transmitter module fabricated in a 0.5 μm silicon-on-sapphire (SOS) technology. The system amplifies, filters, digitizes, and transmits 16 channels of neural data at a rate of 1 Mb/s. The entire system, which includes the VLSI circuits, a digital interface board, a battery, and a custom housing, is small and lightweight (24 g) and, thus, can be chronically mounted on small animals. The system consumes 4.8 mA and records continuously for up to 40 h powered by a 3.7-V, 200-mAh rechargeable lithium-ion battery. Experimental benchtop characterizations as well as in vivo multichannel neural recordings from awake behaving rats are presented here.

Wireless multichannel acquisition of neuropotentials

Implantable brain-machine interfaces for disease diagnosis and motor prostheses control require low-power acquisition of neuropotentials spanning a wide range of amplitudes and frequencies. Here, we present a 16-channel VLSI neuropotential acquisition system with tunable gain and bandwidth, and variable rate digital transmission over an inductive link which further supplies power. The neuropotential interface chip is composed of an amplifier, incremental ADC and bit-serial readout circuitry. The front-end amplifier has a midband gain of 40 dB and offers NEF of less than 3 for all bandwidth settings. It also features adjustable low-frequency cut-off from 0.2 to 94 Hz, and independent high-frequency cut-off from 140 Hz to 8.2 kHz. The Gm-C incremental DeltaSigma ADC offers digital gain up to 4096 and 8-12 bits resolution. The interface circuit is powered by a telemetry chip which harvests power through inductive coupling from a 4 MHz link, provides a 1 MHz clock for ADC operation and transmits the bit-serial data of the neurpotential interface across 4 cm at up to 32 kbps with a BER less than 10-5. Experimental EEG recordings using the neuropotential interface and wireless module are presented.