Review of Brain-Machine Interfaces Used in Neural Prosthetics with New Perspective on Somatosensory Feedback through Method of Signal Breakdown

Abstract

The brain-machine interface (BMI) used in neural prosthetics involves recording signals from neuron populations, decoding those signals using mathematical modeling algorithms, and translating the intended action into physical limb movement. Recently, somatosensory feedback has become the focus of many research groups given its ability in increased neural control by the patient and to provide a more natural sensation for the prosthetics. This process involves recording data from force sensitive locations on the prosthetics and encoding these signals to be sent to the brain in the form of electrical stimulation. Tactile sensation has been achieved through peripheral nerve stimulation and direct stimulation of the somatosensory cortex using intracortical microstimulation (ICMS). The initial focus of this paper is to review these principles and link them to modern day applications such as restoring limb use to those who lack such control. With regard to how far the research has come, a new perspective for the signal breakdown concludes the paper, offering ideas for more real somatosensory feedback using ICMS to stimulate particular sensations by differentiating touch sensors and filtering data based on unique frequencies.

Figure 1

Working of neural prosthetics using a brain-machine interface. Afferent somatosensory signal is taken from the prosthetic device and is fed into the brain, from where the motor signal is sent back to the prosthetic limb [5].

Figure 2

Illustration that depicts the set-up using an exoskeleton to incorporate proprioception into the mind controlled computer curser operated by the monkey [2].

Figure 3

Phantom limb pain depiction. The nerve endings (located at the red circle), still present at the site of the amputation, send signals (red arrows) or the cortical reorganization (red star in the brain) generates the phantom limp pain [17]. Other sensations that can be felt involve tingling, cramping, heat, and cold.

Figure 4

A schematic demonstrating possible filter designs and subsequent processing that could be used to replicate signals from native sensory afferents [13]. The proposed filters for band selection and processing are derived from the properties of sensory afferents from Table 1. SA I signals are shown being replicated by low-pass filtering to 100 Hz; then processing would be applied to replicate as close to 5 Hz steps of sensitivity applicable. RA I shows filtering followed by processing to replicate rapid adaptation as well as the sensitivity levels. RA II signals are shown being replicated by a hypothetical wavelet transform, showing the coefficients for a particular frequency range represented by a wavelet at a particular resolution. Since not much is known about the particular frequency range of SA II signals, it is listed blank. All filter designs and processing methods shown in the figure are hypothetical.