Utilizing Brain-computer Interfacing to Control Neuroprosthetic Devices

Advances in neuroprosthetics in recent years have made an enormous impact on the quality of life for many people with disabilities, helping them regain the functionality of damaged or impaired abilities. One of the main hurdles to regaining full functionality regarding neuroprosthetics is the integration between the neural prosthetic device and the method in which the neural prosthetic device is controlled or manipulated to function correctly and efficiently. One of the most promising methods for integrating neural prosthetics to an efficient method of control is through Brian-computer Interfacing (BCI). With this method, the neuroprosthetic device is integrated into the human brain through the use of a specialized computer, which allows for users of neuroprosthetic devices to control the devices in the same way that they would control a normally working human function- with their mind. There are both invasive and non-invasive methods to implement Brain-computer Interfacing, both of which involve the process of acquiring a brain signal, processing the signal, and finally providing a usable device output. There are several examples of integration between Brain-computer Interfacing and neural prosthetics that are currently being researched. Many challenges must be overcome before a widespread clinical application of integration between Brain-computer Interfaces and neural prosthetics becomes a reality, but current research continues to provide promising advancement toward making this technology available as a means for people to regain lost functionality

Design and testing of a 96-channel neural interface module for the Networked Neuroprosthesis system

Abstract Background The loss of motor functions resulting from spinal cord injury can have devastating implications on the quality of one’s life. Functional electrical stimulation has been used to help restore mobility, however, current functional electrical stimulation (FES) systems require residual movements to control stimulation patterns, which may be unintuitive and not useful for individuals with higher level cervical injuries. Brain machine interfaces (BMI) offer a promising approach for controlling such systems; however, they currently still require transcutaneous leads connecting indwelling electrodes to external recording devices. While several wireless BMI systems have been designed, high signal bandwidth requirements limit clinical translation. Case Western Reserve University has developed an implantable, modular FES system, the Networked Neuroprosthesis (NNP), to perform combinations of myoelectric recording and neural stimulation for controlling motor functions. However, currently the existing module capabilities are not sufficient for intracortical recordings. Methods Here we designed and tested a 1 × 4 cm, 96-channel neural recording module prototype to fit within the specifications to mate with the NNP. The neural recording module extracts power between 0.3–1 kHz, instead of transmitting the raw, high bandwidth neural data to decrease power requirements. Results The module consumed 33.6 mW while sampling 96 channels at approximately 2 kSps. We also investigated the relationship between average spiking band power and neural spike rate, which produced a maximum correlation of R = 0.8656 (Monkey N) and R = 0.8023 (Monkey W). Conclusion Our experimental results show that we can record and transmit 96 channels at 2ksps within the power restrictions of the NNP system and successfully communicate over the NNP network. We believe this device can be used as an extension to the NNP to produce a clinically viable, fully implantable, intracortically-controlled FES system and advance the field of bioelectronic medicine.https://deepblue.lib.umich.edu/bitstream/2027.42/147921/1/42234_2019_Article_19.pd

Brain-Computer Interfaces in Medicine

Brain-computer interfaces (BCIs) acquire brain signals, analyze them, and translate them into commands that are relayed to output devices that carry out desired actions. BCIs do not use normal neuromuscular output pathways. The main goal of BCI is to replace or restore useful function to people disabled by neuromuscular disorders such as amyotrophic lateral sclerosis, cerebral palsy, stroke, or spinal cord injury. From initial demonstrations of electroenceph-alography-based spelling and single-neuron-based device control, researchers have gone on to use electroenceph-alographic, intracortical, electrocorticographic, and other brain signals for increasingly complex control of cursors, robotic arms, prostheses, wheelchairs, and other devices. Brain-computer interfaces may also prove useful for rehabilitation after stroke and for other disorders. In the future, they might augment the performance of surgeons or other medical professionals. Brain-computer interface technology is the focus of a rapidly growing research and development enterprise that is greatly exciting scientists, engineers, clinicians, and the public in general. Its future achievements will depend on advances in 3 crucial areas. Brain-computer interfaces need signal-acquisition hardware that is convenient, portable, safe, and able to function in all environments. Brain-computer interface systems need to be validated in long-term studies of real-world use by people with severe disabilities, and effective and viable models for their widespread dissemination must be implemented. Finally, the day-to-day and moment-to-moment reliability of BCI performance must be improved so that it approaches the reliability of natural muscle-based function

Doctor of Philosophy

dissertationThis dissertation describes the use of cortical surface potentials, recorded with dense grids of microelectrodes, for brain-computer interfaces (BCIs). The work presented herein is an in-depth treatment of a broad and interdisciplinary topic, covering issues from electronics to electrodes, signals, and applications. Within the scope of this dissertation are several significant contributions. First, this work was the first to demonstrate that speech and arm movements could be decoded from surface local field potentials (LFPs) recorded in human subjects. Using surface LFPs recorded over face-motor cortex and Wernickes area, 150 trials comprising vocalized articulations of ten different words were classified on a trial-by-trial basis with 86% accuracy. Surface LFPs recorded over the hand and arm area of motor cortex were used to decode continuous hand movements, with correlation of 0.54 between the actual and predicted position over 70 seconds of movement. Second, this work is the first to make a detailed comparison of cortical field potentials recorded intracortically with microelectrodes and at the cortical surface with both micro- and macroelectrodes. Whereas coherence in macroelectrocorticography (ECoG) decayed to half its maximum at 5.1 mm separation in high frequencies, spatial constants of micro-ECoG signals were 530-700 ?m-much closer to the 110-160 ?m calculated for intracortical field potentials than to the macro-ECoG. These findings confirm that cortical surface potentials contain millimeter-scale dynamics. Moreover, these fine spatiotemporal features were important for the performance of speech and arm movement decoding. In addition to contributions in the areas of signals and applications, this dissertation includes a full characterization of the microelectrodes as well as collaborative work in which a custom, low-power microcontroller, with features optimized for biomedical implants, was taped out, fabricated in 65 nm CMOS technology, and tested. A new instruction was implemented in this microcontroller which reduced energy consumption when moving large amounts of data into memory by as much as 44%. This dissertation represents a comprehensive investigation of surface LFPs as an interfacing medium between man and machine. The nature of this work, in both the breadth of topics and depth of interdisciplinary effort, demonstrates an important and developing branch of engineering

Traumatic brain injury neuroelectrochemical monitoring: behind-the-ear micro-instrument and cloud application

BACKGROUND: Traumatic Brain Injury (TBI) is a leading cause of fatality and disability worldwide, partly due to the occurrence of secondary injury and late interventions. Correct diagnosis and timely monitoring ensure effective medical intervention aimed at improving clinical outcome. However, due to the limitations in size and cost of current ambulatory bioinstruments, they cannot be used to monitor patients who may still be at risk of secondary injury outside the ICU. METHODS: We propose a complete system consisting of a wearable wireless bioinstrument and a cloud-based application for real-time TBI monitoring. The bioinstrument can simultaneously record up to ten channels including both ECoG biopotential and neurochemicals (e.g. potassium, glucose and lactate), and supports various electrochemical methods including potentiometry, amperometry and cyclic voltammetry. All channels support variable gain programming to automatically tune the input dynamic range and address biosensors' falling sensitivity. The instrument is flexible and can be folded to occupy a small space behind the ear. A Bluetooth Low-Energy (BLE) receiver is used to wirelessly connect the instrument to a cloud application where the recorded data is stored, processed and visualised in real-time. Bench testing has been used to validate device performance. RESULTS: The instrument successfully monitored spreading depolarisations (SDs) - reproduced using a signal generator - with an SNR of 29.07 dB and NF of 0.26 dB. The potentiostat generates a wide voltage range from -1.65V to +1.65V with a resolution of 0.8mV and the sensitivity of the amperometric AFE was verified by recording 5 pA currents. Different potassium, glucose and lactate concentrations prepared in lab were accurately measured and their respective working curves were constructed. Finally,the instrument achieved a maximum sampling rate of 1.25 ksps/channel with a throughput of 105 kbps. All measurements were successfully received at the cloud. CONCLUSION: The proposed instrument uniquely positions itself by presenting an aggressive optimisation of size and cost while maintaining high measurement accuracy. The system can effectively extend neuroelectrochemical monitoring to all TBI patients including those who are mobile and those who are outside the ICU. Finally, data recorded in the cloud application could be used to help diagnosis and guide rehabilitation

The future of upper extremity rehabilitation robotics: research and practice

The loss of upper limb motor function can have a devastating effect on people’s lives. To restore upper limb control and functionality, researchers and clinicians have developed interfaces to interact directly with the human body’s motor system. In this invited review, we aim to provide details on the peripheral nerve interfaces and brain‐machine interfaces that have been developed in the past 30 years for upper extremity control, and we highlight the challenges that still remain to transition the technology into the clinical market. The findings show that peripheral nerve interfaces and brain‐machine interfaces have many similar characteristics that enable them to be concurrently developed. Decoding neural information from both interfaces may lead to novel physiological models that may one day fully restore upper limb motor function for a growing patient population.Peer Reviewedhttps://deepblue.lib.umich.edu/bitstream/2027.42/155489/1/mus26860_am.pdfhttps://deepblue.lib.umich.edu/bitstream/2027.42/155489/2/mus26860.pd

OPTIMIZATION OF TIME-RESPONSE AND AMPLIFICATION FEATURES OF EGOTs FOR NEUROPHYSIOLOGICAL APPLICATIONS

In device engineering, basic neuron-to-neuron communication has recently inspired the development of increasingly structured and efficient brain-mimicking setups in which the information flow can be processed with strategies resembling physiological ones. This is possible thanks to the use of organic neuromorphic devices, which can share the same electrolytic medium and adjust reciprocal connection weights according to temporal features of the input signals. In a parallel - although conceptually deeply interconnected - fashion, device engineers are directing their efforts towards novel tools to interface the brain and to decipher its signalling strategies. This led to several technological advances which allow scientists to transduce brain activity and, piece by piece, to create a detailed map of its functions. This effort extends over a wide spectrum of length-scales, zooming out from neuron-to-neuron communication up to global activity of neural populations. Both these scientific endeavours, namely mimicking neural communication and transducing brain activity, can benefit from the technology of Electrolyte-Gated Organic Transistors (EGOTs). Electrolyte-Gated Organic Transistors (EGOTs) are low-power electronic devices that functionally integrate the electrolytic environment through the exploitation of organic mixed ionic-electronic conductors. This enables the conversion of ionic signals into electronic ones, making such architectures ideal building blocks for neuroelectronics. This has driven extensive scientific and technological investigation on EGOTs. Such devices have been successfully demonstrated both as transducers and amplifiers of electrophysiological activity and as neuromorphic units. These promising results arise from the fact that EGOTs are active devices, which widely extend their applicability window over the capabilities of passive electronics (i.e. electrodes) but pose major integration hurdles. Being transistors, EGOTs need two driving voltages to be operated. If, on the one hand, the presence of two voltages becomes an advantage for the modulation of the device response (e.g. for devising EGOT-based neuromorphic circuitry), on the other hand it can become detrimental in brain interfaces, since it may result in a non-null bias directly applied on the brain. If such voltage exceeds the electrochemical stability window of water, undesired faradic reactions may lead to critical tissue and/or device damage. This work addresses EGOTs applications in neuroelectronics from the above-described dual perspective, spanning from neuromorphic device engineering to in vivo brain-device interfaces implementation. The advantages of using three-terminal architectures for neuromorphic devices, achieving reversible fine-tuning of their response plasticity, are highlighted. Jointly, the possibility of obtaining a multilevel memory unit by acting on the gate potential is discussed. Additionally, a novel mode of operation for EGOTs is introduced, enabling full retention of amplification capability while, at the same time, avoiding the application of a bias in the brain. Starting on these premises, a novel set of ultra-conformable active micro-epicortical arrays is presented, which fully integrate in situ fabricated EGOT recording sites onto medical-grade polyimide substrates. Finally, a whole organic circuitry for signal processing is presented, exploiting ad-hoc designed organic passive components coupled with EGOT devices. This unprecedented approach provides the possibility to sort complex signals into their constitutive frequency components in real time, thereby delineating innovative strategies to devise organic-based functional building-blocks for brain-machine interfaces.Nell’ingegneria elettronica, la comunicazione di base tra neuroni ha recentemente ispirato lo sviluppo di configurazioni sempre più articolate ed efficienti che imitano il cervello, in cui il flusso di informazioni può essere elaborato con strategie simili a quelle fisiologiche. Ciò è reso possibile grazie all'uso di dispositivi neuromorfici organici, che possono condividere lo stesso mezzo elettrolitico e regolare i pesi delle connessioni reciproche in base alle caratteristiche temporali dei segnali in ingresso. In modo parallelo, gli ingegneri elettronici stanno dirigendo i loro sforzi verso nuovi strumenti per interfacciare il cervello e decifrare le sue strategie di comunicazione. Si è giunti così a diversi progressi tecnologici che consentono agli scienziati di trasdurre l'attività cerebrale e, pezzo per pezzo, di creare una mappa dettagliata delle sue funzioni. Entrambi questi ambiti scientifici, ovvero imitare la comunicazione neurale e trasdurre l'attività cerebrale, possono trarre vantaggio dalla tecnologia dei transistor organici a base elettrolitica (EGOT). I transistor organici a base elettrolitica (EGOT) sono dispositivi elettronici a bassa potenza che integrano funzionalmente l'ambiente elettrolitico attraverso lo sfruttamento di conduttori organici misti ionici-elettronici, i quali consentono di convertire i segnali ionici in segnali elettronici, rendendo tali dispositivi ideali per la neuroelettronica. Gli EGOT sono stati dimostrati con successo sia come trasduttori e amplificatori dell'attività elettrofisiologica e sia come unità neuromorfiche. Tali risultati derivano dal fatto che gli EGOT sono dispositivi attivi, al contrario dell'elettronica passiva (ad esempio gli elettrodi), ma pongono comunque qualche ostacolo alla loro integrazione in ambiente biologico. In quanto transistor, gli EGOT necessitano l'applicazione di due tensioni tra i suoi terminali. Se, da un lato, la presenza di due tensioni diventa un vantaggio per la modulazione della risposta del dispositivo (ad esempio, per l'ideazione di circuiti neuromorfici basati su EGOT), dall'altro può diventare dannosa quando gli EGOT vengono adoperati come sito di registrazione nelle interfacce cerebrali, poiché una tensione non nulla può essere applicata direttamente al cervello. Se tale tensione supera la finestra di stabilità elettrochimica dell'acqua, reazioni faradiche indesiderate possono manifestarsi, le quali potrebbero danneggiare i tessuti e/o il dispositivo. Questo lavoro affronta le applicazioni degli EGOT nella neuroelettronica dalla duplice prospettiva sopra descritta: ingegnerizzazione neuromorfica ed implementazione come interfacce neurali in applicazioni in vivo. Vengono evidenziati i vantaggi dell'utilizzo di architetture a tre terminali per i dispositivi neuromorfici, ottenendo una regolazione reversibile della loro plasticità di risposta. Si discute inoltre la possibilità di ottenere un'unità di memoria multilivello agendo sul potenziale di gate. Viene introdotta una nuova modalità di funzionamento per gli EGOT, che consente di mantenere la capacità di amplificazione e, allo stesso tempo, di evitare l'applicazione di una tensione all’interfaccia cervello-dispositivo. Partendo da queste premesse, viene presentata una nuova serie di array micro-epicorticali ultra-conformabili, che integrano completamente i siti di registrazione EGOT fabbricati in situ su substrati di poliimmide. Infine, viene proposto un circuito organico per l'elaborazione del segnale, sfruttando componenti passivi organici progettati ad hoc e accoppiati a dispositivi EGOT. Questo approccio senza precedenti offre la possibilità di filtrare e scomporre segnali complessi nelle loro componenti di frequenza costitutive in tempo reale, delineando così strategie innovative per concepire blocchi funzionali a base organica per le interfacce cervello-macchina