Po T. Wang

In rehabilitation research, coarse clinical outcome measures may limit the sensitivity of trials by failing to detect meaningful intervention-induced changes. Sensorized robotic platforms can potentially improve the evaluation of motor function, yet their reliability compared to skilled therapists remains unclear. This study utilized a robotic device to measure two fundamental impairments that are critical to ankle function: range of motion (ROM, active and passive) and dorsiflexion maximum voluntary contraction (MVC). In 34 chronic hemiparetic post-stroke individuals, we assessed test–retest reliability over two days for the robot and experienced therapists, who used a goniometer and manual muscle testing (MMT). We also evaluated robotic test–retest reliability in 36 young and 26 older unimpaired adults. Reliability for robotic and therapist-based AROM and MVC measures was high (ICC > 0.86), consistent across all groups. Robotic AROM and PROM measurements correlated strongly with therapist assessments (r > 0.60, p < 0.001) but were 120% and 37% larger than therapist assessments (p < 0.001), respectively. For the MVC measurement, the therapist assigned 85% of participants a score of 1 on the MMT, but their MVC torque was distributed from 0 to ~20 Nm. Measurement differences between methods likely arose from the robot’s constrained setup, allowing for compensatory muscle activation. The increased granularity provided by robotic MVC measurements could enable more precise tracking of motor recovery and facilitate tailored rehabilitation strategies. These results support the clinical utility of robotic platforms for ankle assessment, offering detailed, objective measurements that can augment traditional evaluations, and provide a benchmark dataset for other researchers.

In rehabilitation research, coarse clinical outcome measures may limit the sensitivity of trials by failing to detect meaningful intervention-induced changes. Sensorized robotic platforms can potentially improve the evaluation of motor function, yet their reliability compared to skilled therapists remains unclear. This study utilized a robotic device to measure two fundamental impairments that are critical to ankle function: range of motion (ROM, active and passive) and dorsiflexion maximum voluntary contraction (MVC). In 34 chronic hemiparetic post-stroke individuals, we assessed test-retest reliability over two days for the robot and experienced therapists, who used a goniometer and manual muscle testing (MMT). We also evaluated robotic test-retest reliability in 36 young and 26 older unimpaired adults. Reliability for robotic and therapist-based AROM and MVC measures was high (ICC > 0.86), consistent across all groups. Robotic AROM and PROM measure-ments correlated strongly with therapist assessments (r > 0.60, p < 0.001) but were 120% and 37% larger than therapist assessments (p < 0.001), respectively. For the MVC measurement, the therapist assigned 85% of participants a score of 1 on the MMT, but their MVC torque was distributed from 0 to ~20 Nm. Measurement differences between methods likely arose from the robot's constrained setup, allowing compensatory muscle activation. The increased granularity provided by robotic MVC measurements could enable more precise tracking of motor recovery and facilitate tailored rehabilitation strategies. These results support the clinical utility of robotic platforms for ankle assessment, offering detailed, objective measurements that can augment traditional evaluations. A supplementary dataset for benchmarking future research is provided.

Background: After stroke, ankle proprioceptive deficits are common and do not typically correlate with ankle weakness. Some studies report that these deficits correlate with gait function, supporting the importance of somatosensory input for gait control. Others have not found a relationship, possibly due to use of coarse proprioception measures. Robotic assessments of proprioception offer improved consistency and sensitivity. Objective: To establish relationships between ankle proprioception, gait function, and ankle motor in stroke survivors. Methods: We studied 39 individuals in the chronic phase of stroke using 2 robotic tests, Crisscross and Joint Position Reproduction (JPR), to quantify ankle proprioception. We examined associations of these measures with gait speed (10-meter walk test) and gait endurance (6-minute walk test). We also analyzed correlations with lower extremity motor impairment, including robotic measures of ankle strength (MVC) and active range of motion (AROM), and the lower extremity Fugl-Meyer exam (LEFM). Results: Impaired ankle proprioception was present in 87% of participants. Crisscross error weakly correlated with the 10mWT gait speed (ρ = −0.20, P = 0.23) and 6MWT distance (ρ = −0.28, P = .089). JPR error weakly correlated with 10mWT gait speed (ρ = −0.29, P = .092) and significantly correlated with 6MWT distance (ρ = −0.34, P = .04). No significant correlations were observed between ankle proprioceptive error and MVC, AROM, or LEFM ( P > 0.2). Conclusion: These results confirm the presence of a weak relationship between ankle proprioception and gait after stroke that is independent of several common measures of motor impairment.

Invasive brain-computer interface (BCI) technology has demonstrated the possibility of restoring brain-controlled walking in paraplegic spinal cord injury patients. However, current implementations of BCI-controlled walking still have significant drawbacks. In particular, prior systems are unidirectional and lack sensory feedback for insensate patients, have suboptimal reliance on brain signals from the bilateral arm areas of the motor cortex, and depend on external systems for signal processing. Motivated by these shortcomings, this study is the first time a bidirectional brain-computer interface (BDBCI) has demonstrated the restoration of both brain-controlled walking and leg sensory feedback while utilizing the bilateral leg motor and sensory cortices. Here, a subject undergoing subdural electrocorticogram electrode implantation for epilepsy surgery evaluation leveraged the leg representation areas of the bilateral interhemispheric primary motor and sensory cortices to operate a BDBCI with high performance. Although electrode implantation in the interhemispheric region is uncommon, electrodes can be safely implanted in this region to access rich leg motor information and deliver bilateral leg sensory feedback. Finally, we demonstrated that all BDBCI operations can be executed on a dedicated, portable embedded system. These results indicate that BDBCIs can potentially provide brain-controlled ambulation and artificial leg sensation to people with paraplegia after spinal cord injury in a manner that emulates full-implantability and is untethered from any external systems.

Objective. Invasive brain–computer interfaces (BCIs) have shown promise in restoring motor function to those paralyzed by neurological injuries. These systems also have the ability to restore sensation via cortical electrostimulation. Cortical stimulation produces strong artifacts that can obscure neural signals or saturate recording amplifiers. While front-end hardware techniques can alleviate this problem, residual artifacts generally persist and must be suppressed by back-end methods. Approach. We have developed a technique based on pre-whitening and null projection (PWNP) and tested its ability to suppress stimulation artifacts in electroencephalogram (EEG), electrocorticogram (ECoG) and microelectrode array (MEA) signals from five human subjects. Main results. In EEG signals contaminated by narrow-band stimulation artifacts, the PWNP method achieved average artifact suppression between 32 and 34 dB, as measured by an increase in signal-to-interference ratio. In ECoG and MEA signals contaminated by broadband stimulation artifacts, our method suppressed artifacts by 78%–80% and 85%, respectively, as measured by a reduction in interference index. When compared to independent component analysis, which is considered the state-of-the-art technique for artifact suppression, our method achieved superior results, while being significantly easier to implement. Significance. PWNP can potentially act as an efficient method of artifact suppression to enable simultaneous stimulation and recording in bi-directional BCIs to biomimetically restore motor function.

Objective . State-of-the-art invasive brain-machine interfaces (BMIs) have shown significant promise, but rely on external electronics and wired connections between the brain and these external components. This configuration presents health risks and limits practical use. These limitations can be addressed by designing a fully implantable BMI similar to existing FDA-approved implantable devices. Here, a prototype BMI system whose size and power consumption are comparable to those of fully implantable medical devices was designed and implemented, and its performance was tested at the benchtop and bedside. Approach . A prototype of a fully implantable BMI system was designed and implemented as a miniaturized embedded system. This benchtop analogue was tested in its ability to acquire signals, train a decoder, perform online decoding, wirelessly control external devices, and operate independently on battery. Furthermore, performance metrics such as power consumption were benchmarked. Main results . An analogue of a fully implantable BMI was fabricated with a miniaturized form factor. A patient undergoing epilepsy surgery evaluation with an electrocorticogram (ECoG) grid implanted over the primary motor cortex was recruited to operate the system. Seven online runs were performed with an average binary state decoding accuracy of 87.0% (lag optimized, or 85.0% at fixed latency). The system was powered by a wirelessly rechargeable battery, consumed ∼150 mW, and operated for >60 h on a single battery cycle. Significance . The BMI analogue achieved immediate and accurate decoding of ECoG signals underlying hand movements. A wirelessly rechargeable battery and other supporting functions allowed the system to function independently. In addition to the small footprint and acceptable power and heat dissipation, these results suggest that fully implantable BMI systems are feasible.

Brain-machine interfaces (BMIs) translate brain signals into control signals for an external device, such as a computer cursor or robotic limb. These signals can be obtained either noninvasively or invasively. Invasive recordings, using electrocorticography (ECoG) or intracortical microelectrodes, provide higher bandwidth and more informative signals. Rehabilitative BMIs, which aim to drive plasticity in the brain to enhance recovery after brain injury, have almost exclusively used non-invasive recordings, such electroencephalography (EEG) or magnetoencephalography (MEG), which have limited bandwidth and information content. Invasive recordings provide more information and spatiotemporal resolution, but do incur risk, and thus are not usually investigated in people with stroke or traumatic brain injury (TBI). Here, in this paper, we describe a new BMI paradigm to investigate the use of higher frequency signals in brain-injured subjects without incurring significant risk. We recorded EEG in TBI subjects who required hemicraniectomies (removal of a part of the skull). EEG over the hemicraniectomy (hEEG) contained substantial information in the high gamma frequency range (65-115 Hz). Using this information, we decoded continuous finger flexion force with moderate to high accuracy (variance accounted for 0.06 to 0.52), which at best approaches that using epidural signals. These results indicate that people with hemicraniectomies can provide a useful resource for developing BMI therapies for the treatment of brain injury.

A MedRadio 413-419-MHz inductorless transceiver (TRX) for an implantable brain-machine interface (BMI) in a 180 nm-CMOS process is presented. Occupying 5.29 mm ² of die area (including pad ring), this on-off keying (OOK) TRX employs a non-coherent direct-detection receiver (RX), which exhibits a measured in-band noise figure (NF) of 4.9 dB and S ₁₁ of -13.5 dB. An event-driven supply modulation (EDSM) technique is introduced to dramatically lower the RX power consumption. Incorporating an adaptive feedback loop, this RX consumes 42-/92-μW power from 1.8-V supply at 1/10-kbps data rates, achieving -79/-74-dBm sensitivities for 0.1% bit error rate (BER). The TX employs a current starved ring oscillator with an automatic frequency calibration loop, covering 9% supply voltage variation and 15 °C-78 °C temperature range that guarantees operation within the emission mask. The direct-modulation TX achieves 14% efficiency for a random OOK data sequence at -4-dBm output power. Wireless testing over a 350-cm distance accounting for bio-signal data transfer, multi-user coexistence, and in vitro phantom measurement results is demonstrated.

An inductorless MedRadio (413~419 MHz) OOK transceiver for an implantable brain-machine interface (BMI) in 180 nm CMOS is presented. An event-driven supply modulation (EDSM) technique is introduced in the receiver to lower the power consumption to 42/92 μW at 1/10 kbps, achieving -79/-74 dBm sensitivity (at 0.1% BER). The transmitter employs a current-starved ring oscillator with an automatic frequency calibration loop, achieving 14% efficiency for random OOK data at -4 dBm output power. Wireless connection, benchtop, multi-user coexistence, and in-vitro phantom test results are demonstrated.

A fully implantable brain-computer interface (BCI) can be a practical tool to restore independence to those affected by spinal cord injury. We envision that such a BCI system will invasively acquire brain signals (e.g. electrocorticogram) and translate them into control commands for external prostheses. The feasibility of such a system was tested by implementing its benchtop analogue, centered around a commercial, ultra-low power (ULP) digital signal processor (DSP, TMS320C5517, Texas Instruments). A suite of signal processing and BCI algorithms, including (de)multiplexing, Fast Fourier Transform, power spectral density, principal component analysis, linear discriminant analysis, Bayes rule, and finite state machine was implemented and tested in the DSP. The system's signal acquisition fidelity was tested and characterized by acquiring harmonic signals from a function generator. In addition, the BCI decoding performance was tested, first with signals from a function generator, and subsequently using human electroencephalogram (EEG) during eyes opening and closing task. On average, the system spent 322 ms to process and analyze 2 s of data. Crosstalk (<;-65 dB) and harmonic distortion (~1%) were minimal. Timing jitter averaged 49 μs per 1000 ms. The online BCI decoding accuracies were 100% for both function generator and EEG data. These results show that a complex BCI algorithm can be executed on an ULP DSP without compromising performance. This suggests that the proposed hardware platform may be used as a basis for future, fully implantable BCI systems.