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The sense of prosthesis embodiment, or the feeling that the device has been incorporated into a user's body image, may be enhanced by emerging technology such as invasive electrical stimulation for sensory feedback. In turn, prosthesis embodiment may be linked to increased prosthesis use and improved functional outcomes. We describe the development of a tool to assay artificial hand embodiment in a quantitative way in people with intact limbs, and characterize its operation. The system delivers temporally coordinated visual and tactile stimuli at a programmable latency while recording limb temperature. When programmed to deliver visual and tactile stimuli synchronously, recorded latency between the two was 33 ± 24 ms in the final pilot subject. This system enables standardized assays of the conditions necessary for prosthesis embodiment.

The needs of individuals with upper limb amputation and congenital limb difference are not being fully met by current prostheses, as evidenced by prosthesis rejection, non-wear, and user reports of pain and challenging activities. Emerging technologies such as dexterous sensorized robotic limbs, osseointegrated prostheses, implantable EMG electrodes, and electrical stimulation for sensory feedback have the potential to address unmet needs, but pose additional risks. We plan to assess upper limb prosthesis user needs and perspectives on these new benefits and risks using an extensive quantitative survey. In preparation for this survey, we report here on qualitative interviews with seven individuals with upper limb amputation or congenital limb difference. Unstructured text was mined using topic modeling and the results compared with identified themes. A more complete understanding of how novel technologies could address real user concerns will inform implementation of new technologies and regulatory decision-making.

Advanced upper limb prosthetics, such as the Johns Hopkins Applied Physics Lab Modular Prosthetic Limb (MPL), are now available for research and preliminary clinical applications. Research attention has shifted to developing means of controlling these prostheses. Penetrating microelectrode arrays are often used in animal and human models to decode action potentials for cortical control. These arrays may suffer signal loss over the long-term and therefore should not be the only implant type investigated for chronic BMI use. Electrocorticographic (ECoG) signals from electrodes on the cortical surface may provide more stable long-term recordings. Several studies have demonstrated ECoG's potential for decoding cortical activity. As a result, clinical studies are investigating ECoG encoding of limb movement, as well as its use for interfacing with and controlling advanced prosthetic arms. This overview presents the technical state of the art in the use of ECoG in controlling prostheses. Technical limitations of the current approach and future directions are also presented.

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.

While significant strides have been made in designing brain-machine interfaces for use in humans, efforts to decode truly dexterous movements in real time have been hindered by difficulty extracting detailed movement-related information from the most practical human neural interface, the electrocorticogram (ECoG). We explore a potentially rich, largely untapped source of movement-related information in the form of cortical connectivity computed with time-varying dynamic Bayesian networks (TV-DBN). We discover that measures of connectivity between ECoG electrodes derived from the local motor potential vary with dexterous movement in 65% of movement-related electrode pairs tested, and measures of connectivity derived from spectral features vary with dexterous movement in 76%. Due to the large number of features generated with connectivity methods, the TV-DBN a promising tool for dexterous decoding.

As a partially invasive and clinically obtained neural signal, the electrocorticogram (ECoG) provides a unique opportunity to study cortical processing in humans in vivo. Functional connectivity mapping based on the ECoG signal can provide insight into epileptogenic zones and putative cortical circuits. We describe the first application of time-varying dynamic Bayesian networks (TVDBN) to the ECoG signal for the identification and study of cortical circuits. Connectivity between motor areas as well as between sensory and motor areas preceding and during movement is described. We further apply the connectivity results of the TVDBN to a movement decoder, which achieves a correlation between actual and predicted hand movements of 0.68. This paper presents evidence that the connectivity information discovered with TVDBN is applicable to the design of an ECoG-based brain-machine interface.