A feasibility study of a complete low-cost consumer-grade brain-computer interface system

Brain-computer interfaces (BCIs) are technologies that provide the user with an alternative way of communication. A BCI measures brain activity (e.g. EEG) and converts it into output commands. Motor imagery (MI), the mental simulation of movements, can be used as a BCI paradigm, where the movement intention of the user can be translated into a real movement, helping patients in motor recovery rehabilitation. One of the main limitations for the broad use of such devices is the high cost associated with the high-quality equipment used for capturing the biomedical signals. Different low-cost consumer-grade alternatives have emerged with the objective of bringing these systems closer to the final users. The quality of the signals obtained with such equipments has already been evaluated and found to be competitive with those obtained with well-known clinical-grade devices. However, how these consumer-grade technologies can be integrated and used for practical MI-BCIs has not yet been explored. In this work, we provide a detailed description of the advantages and disadvantages of using OpenBCI boards, low-cost sensors and open-source software for constructing an entirely consumer-grade MI-BCI system. An analysis of the quality of the signals acquired and the MI detection ability is performed. Even though communication between the computer and the OpenBCI board is not always stable and the signal quality is sometimes affected by ambient noise, we find that by means of a filter-bank based method, similar classification performances can be achieved with an MI-BCI built under low-cost consumer-grade devices as compared to when clinical-grade systems are used. By means of this work we share with the BCI community our experience on working with emerging low-cost technologies, providing evidence that an entirely low-cost MI-BCI can be built. We believe that if communication stability and artifact rejection are improved, these technologies will become a valuable alternative to clinical-grade devices.Fil: Peterson, Victoria. Universidad Nacional de Entre Ríos; ArgentinaFil: Galván, Catalina María. Universidad Nacional del Litoral; ArgentinaFil: Hernández, Hugo. Universidad Nacional de Entre Ríos; ArgentinaFil: Spies, Ruben Daniel. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Santa Fe. Instituto de Matemática Aplicada del Litoral. Universidad Nacional del Litoral. Instituto de Matemática Aplicada del Litoral; Argentin

Induction of Neural Plasticity Using a Low-Cost Open Source Brain-Computer Interface and a 3D-Printed Wrist Exoskeleton

Brain-computer interfaces (BCIs) have been proven to be useful for stroke rehabilitation, but there are a number of factors that impede the use of this technology in rehabilitation clinics and in home-use, the major factors including the usability and costs of the BCI system. The aims of this study were to develop a cheap 3D-printed wrist exoskeleton that can be controlled by a cheap open source BCI (OpenViBE), and to determine if training with such a setup could induce neural plasticity. Eleven healthy volunteers imagined wrist extensions, which were detected from single-trial electroencephalography (EEG), and in response to this, the wrist exoskeleton replicated the intended movement. Motor-evoked potentials (MEPs) elicited using transcranial magnetic stimulation were measured before, immediately after, and 30 min after BCI training with the exoskeleton. The BCI system had a true positive rate of 86 ± 12% with 1.20 ± 0.57 false detections per minute. Compared to the measurement before the BCI training, the MEPs increased by 35 ± 60% immediately after and 67 ± 60% 30 min after the BCI training. There was no association between the BCI performance and the induction of plasticity. In conclusion, it is possible to detect imaginary movements using an open-source BCI setup and control a cheap 3D-printed exoskeleton that when combined with the BCI can induce neural plasticity. These findings may promote the availability of BCI technology for rehabilitation clinics and home-use. However, the usability must be improved, and further tests are needed with stroke patients

Low-Power Integrated Circuits For Biomedical Applications

With thousands new cases of spinal cord injury reported everyday, many people suffer from paralysis and loss of sensation in both legs. Beside the healthcare costs, such a state severely deteriorates the patients' quality of life and may even lead to additional medical conditions. Therefore, there is a growing need for cyber-physical systems to restore the walking ability through bypassing the damaged spinal cord. This goal can be achieved by monitoring and processing patient's brain signals to enable brain-directed control of prosthetic legs. Among several existing methods to record brain signals, electrocorticography (ECoG) has gained popularity due to being robust to motion artifacts, having high spatial resolution and signal to noise ratio, being moderately invasive and the possibility of chronic implantation of recording grids with no or minor scar tissue formation. The latest property is of particular importance for the whole system to be a viable fully implantable solution. Furthermore, the implanted system has to operate independently with no or minimal need of external hardware (e.g. a bulky personal computer) to be individually and socially accepted. To implement a fully implantable system, low-power and miniaturized electronics are needed to reduced heat generation, increase battery life-time and be minimally intrusive. These requirements indicate that many of the system's components should be custom-designed to integrated as much functionality as possible in a given real estate. This thesis presents silicon tested prototypes of several building blocks for the envisioned system, namely, ultra low-power brain signal acquisition front-ends, a low-power and inductorless MedRadio transceiver, and a fast start-up crystal oscillator. Brain signal acquisition front-ends provide low noise amplification of weak ECoG biosignals. MedRadio transceiver enables communication between the implant and end effectors or base station (e.g. prosthetic legs or desktop computer). Crystal oscillator generates the reference signal for other system's components such as analog to digital converter. Novel techniques to improve important performance parameters (power consumption, low noise operation and interference resilience) have been introduced. Electrical, in-vitro and in-vivo experimental measurements have verified the functionality and performance of each design

Performance Assessment of a Custom, Portable, and Low-Cost Brain–Computer Interface Platform

ObjectiveConventional brain-computer interfaces (BCIs) are often expensive, complex to operate, and lack portability, which confines their use to laboratory settings. Portable, inexpensive BCIs can mitigate these problems, but it remains unclear whether their low-cost design compromises their performance. Therefore, we developed a portable, low-cost BCI and compared its performance to that of a conventional BCI.MethodsThe BCI was assembled by integrating a custom electroencephalogram (EEG) amplifier with an open-source microcontroller and a touchscreen. The function of the amplifier was first validated against a commercial bioamplifier, followed by a head-to-head comparison between the custom BCI (using four EEG channels) and a conventional 32-channel BCI. Specifically, five able-bodied subjects were cued to alternate between hand opening/closing and remaining motionless while the BCI decoded their movement state in real time and provided visual feedback through a light emitting diode. Subjects repeated the above task for a total of 10 trials, and were unaware of which system was being used. The performance in each trial was defined as the temporal correlation between the cues and the decoded states.ResultsThe EEG data simultaneously acquired with the custom and commercial amplifiers were visually similar and highly correlated ( ρ = 0.79). The decoding performances of the custom and conventional BCIs averaged across trials and subjects were 0.70 ± 0.12 and 0.68 ± 0.10, respectively, and were not significantly different.ConclusionThe performance of our portable, low-cost BCI is comparable to that of the conventional BCIs.SignificancePlatforms, such as the one developed here, are suitable for BCI applications outside of a laboratory

Performance Assessment of a Custom, Portable, and Low-Cost Brain-Computer Interface Platform.

ObjectiveConventional brain-computer interfaces (BCIs) are often expensive, complex to operate, and lack portability, which confines their use to laboratory settings. Portable, inexpensive BCIs can mitigate these problems, but it remains unclear whether their low-cost design compromises their performance. Therefore, we developed a portable, low-cost BCI and compared its performance to that of a conventional BCI.MethodsThe BCI was assembled by integrating a custom electroencephalogram (EEG) amplifier with an open-source microcontroller and a touchscreen. The function of the amplifier was first validated against a commercial bioamplifier, followed by a head-to-head comparison between the custom BCI (using four EEG channels) and a conventional 32-channel BCI. Specifically, five able-bodied subjects were cued to alternate between hand opening/closing and remaining motionless while the BCI decoded their movement state in real time and provided visual feedback through a light emitting diode. Subjects repeated the above task for a total of 10 trials, and were unaware of which system was being used. The performance in each trial was defined as the temporal correlation between the cues and the decoded states.ResultsThe EEG data simultaneously acquired with the custom and commercial amplifiers were visually similar and highly correlated ( ρ = 0.79). The decoding performances of the custom and conventional BCIs averaged across trials and subjects were 0.70 ± 0.12 and 0.68 ± 0.10, respectively, and were not significantly different.ConclusionThe performance of our portable, low-cost BCI is comparable to that of the conventional BCIs.SignificancePlatforms, such as the one developed here, are suitable for BCI applications outside of a laboratory

User-Centered Design Strategies for Clinical Brain-Computer Interface Assistive Technology Devices

Although in the past 50 years significant advances based on research of brain-computer interface (BCI) technology have occurred, there is a scarcity of BCI assistive technology devices at the consumer level. This multiple case study explored user-centered clinical BCI device design strategies used by computer scientists designing BCI assistive technologies to meet patient-centered outcomes. The population for the study encompassed computer scientists experienced with clinical BCI assistive technology design located in the midwestern, northeastern, and southern regions of the United States, as well as western Europe. The multi-motive information systems continuance model was the conceptual framework for the study. Interview data were collected from 7 computer scientists and 28 archival documents. Guided by the concepts of user-centered design and patient-centered outcomes, thematic analysis was used to identify codes and themes related to computer science and the design of BCI assistive technology devices. Notable themes included customization of clinical BCI devices, consideration of patient/caregiver interaction, collective data management, and evolving technology. Implications for social change based on the findings from this research include focus on meeting individualized patient-centered outcomes; enhancing collaboration between researchers, caregivers, and patients in BCI device development; and reducing the possibility of abandonment or disuse of clinical BCI assistive technology devices