40 research outputs found
An analysis of electromyography as an input method for resilient and affordable systems: human-computer interfacing using the bodyβs electrical activity
This article was published in the Spring 2014 issue of the Journal of Undergraduate Researc
A Decentralized ComBat Algorithm and Applications to Functional Network Connectivity
Recent studies showed that working with neuroimage data collected from different research facilities or locations may incur additional source dependency, affecting the overall statistical power. This problem can be mitigated with data harmonization approaches. Recently, the ComBat method has become commonly adopted for various neuroimage modalities. While open neuroimaging datasets are becoming more common, a substantial amount of data is still unable to be shared for various reasons. In addition, current approaches require moving all the data to a central location, which requires additional resources and creates redundant copies of the same datasets. To address these issues, we propose a decentralized harmonization approach that does not create redundant copies of the original datasets and performs remote operations on the datasets separately without sharing any individual subject data, ensuring a certain level of privacy and reducing regulatory hurdles. We proposed a novel approach called βDecentralized ComBatβ which can harmonize datasets separately without combining the datasets. We tested our model by harmonizing functional network connectivity datasets from two traumatic brain injury studies in a decentralized way. Also, we used simulations to analyze the performance and scalability of our model when the number of data collection sites increases. We compare the output with centralized ComBat and show that the proposed approach produces similar results, increasing the sensitivity of the functional network connectivity analysis and validating our approach. Simulations show that our model can be easily scaled to many more datasets based on the requirement. In sum, we believe this provides a powerful tool, further complementing open data and allowing for integrating public and private datasets
A Decentralized ComBat Algorithm and Applications to Functional Network Connectivity
Recent studies showed that working with neuroimage data collected from different research facilities or locations may incur additional source dependency, affecting the overall statistical power. This problem can be mitigated with data harmonization approaches. Recently, the ComBat method has become commonly adopted for various neuroimage modalities. While open neuroimaging datasets are becoming more common, a substantial amount of data is still unable to be shared for various reasons. In addition, current approaches require moving all the data to a central location, which requires additional resources and creates redundant copies of the same datasets. To address these issues, we propose a decentralized harmonization approach that does not create redundant copies of the original datasets and performs remote operations on the datasets separately without sharing any individual subject data, ensuring a certain level of privacy and reducing regulatory hurdles. We proposed a novel approach called "Decentralized ComBat " which can harmonize datasets separately without combining the datasets. We tested our model by harmonizing functional network connectivity datasets from two traumatic brain injury studies in a decentralized way. Also, we used simulations to analyze the performance and scalability of our model when the number of data collection sites increases. We compare the output with centralized ComBat and show that the proposed approach produces similar results, increasing the sensitivity of the functional network connectivity analysis and validating our approach. Simulations show that our model can be easily scaled to many more datasets based on the requirement. In sum, we believe this provides a powerful tool, further complementing open data and allowing for integrating public and private datasets.</p
Post-stroke Rehabilitation of Severe Upper Limb Paresis in Germany β Toward Long-Term Treatment With Brain-Computer Interfaces
Severe upper limb paresis can represent an immense burden for stroke survivors. Given the rising prevalence of stroke, restoration of severe upper limb motor impairment remains a major challenge for rehabilitation medicine because effective treatment strategies are lacking. Commonly applied interventions in Germany, such as mirror therapy and impairment-oriented training, are limited in efficacy, demanding for new strategies to be found. By translating brain signals into control commands of external devices, brain-computer interfaces (BCIs) and brain-machine interfaces (BMIs) represent promising, neurotechnology-based alternatives for stroke patients with highly restricted arm and hand function. In this mini-review, we outline perspectives on how BCI-based therapy can be integrated into the different stages of neurorehabilitation in Germany to meet a long-term treatment approach: We found that it is most appropriate to start therapy with BCI-based neurofeedback immediately after early rehabilitation. BCI-driven functional electrical stimulation (FES) and BMI robotic therapy are well suited for subsequent post hospital curative treatment in the subacute stage. BCI-based hand exoskeleton training can be continued within outpatient occupational therapy to further improve hand function and address motivational issues in chronic stroke patients. Once the rehabilitation potential is exhausted, BCI technology can be used to drive assistive devices to compensate for impaired function. However, there are several challenges yet to overcome before such long-term treatment strategies can be implemented within broad clinical application: 1. developing reliable BCI systems with better usability; 2. conducting more research to improve BCI training paradigms and 3. establishing reliable methods to identify suitable patients
ΠΠ½ΡΠ΅Π»Π»Π΅ΠΊΡΡΠ°Π»ΡΠ½ΠΎΠ΅ ΠΊΡΠ΅ΡΠ»ΠΎ-ΡΠΎΠ±ΠΎΡ ΡΠΎ Π²ΡΠΏΠΎΠΌΠΎΠ³Π°ΡΠ΅Π»ΡΠ½ΡΠΌΠΈ ΡΡΠ΅Π΄ΡΡΠ²Π°ΠΌΠΈ ΡΠ²ΡΠ·ΠΈ Ρ ΠΈΡΠΏΠΎΠ»ΡΠ·ΠΎΠ²Π°Π½ΠΈΠ΅ΠΌ ΠΎΡΠΊΠ»ΠΈΠΊΠΎΠ² TEP ΠΈ Ρ Π°ΡΠ°ΠΊΡΠ΅ΡΠΈΡΡΠΈΠΊ Π΄ΠΈΠ°ΠΏΠ°Π·ΠΎΠ½Π° ΡΠΏΠ΅ΠΊΡΡΠ° Π±ΠΎΠ»Π΅Π΅ Π²ΡΡΠΎΠΊΠΎΠ³ΠΎ ΠΏΠΎΡΡΠ΄ΠΊΠ°
In recent years, electroencephalography-based navigation and communication systems for differentially enabled communities have been progressively receiving more attention. To provide a navigation system with a communication aid, a customized protocol using thought evoked potentials has been proposed in this research work to aid the differentially enabled communities. This study presents the higher order spectra based features to categorize seven basic tasks that include Forward, Left, Right, Yes, NO, Help and Relax; that can be used for navigating a robot chair and also for communications using an oddball paradigm. The proposed system records the eight-channel wireless electroencephalography signal from ten subjects while the subject was perceiving seven different tasks. The recorded brain wave signals are pre-processed to remove the interference waveforms and segmented into six frequency band signals, i. e. Delta, Theta, Alpha, Beta, Gamma 1-1 and Gamma 2. The frequency band signals are segmented into frame samples of equal length and are used to extract the features using bispectrum estimation. Further, statistical features such as the average value of bispectral magnitude and entropy using the bispectrum field are extracted and formed as a feature set. The extracted feature sets are tenfold cross validated using multilayer neural network classifier. From the results, it is observed that the entropy of bispectral magnitude feature based classifier model has the maximum classification accuracy of 84.71 % and the value of the bispectral magnitude feature based classifier model has the minimum classification accuracy of 68.52 %.Π ΠΏΠΎΡΠ»Π΅Π΄Π½ΠΈΠ΅ Π³ΠΎΠ΄Ρ Π²ΡΠ΅ Π±ΠΎΠ»ΡΡΠ΅ Π²Π½ΠΈΠΌΠ°Π½ΠΈΡ ΡΠ΄Π΅Π»ΡΠ΅ΡΡΡ Π½Π°Π²ΠΈΠ³Π°ΡΠΈΠΎΠ½Π½ΡΠΌ ΠΈ ΠΊΠΎΠΌΠΌΡΠ½ΠΈΠΊΠ°ΡΠΈΠΎΠ½Π½ΡΠΌ ΡΠΈΡΡΠ΅ΠΌΠ°ΠΌ Π½Π° ΠΎΡΠ½ΠΎΠ²Π΅ ΡΠ»Π΅ΠΊΡΡΠΎΡΠ½ΡΠ΅ΡΠ°Π»ΠΎΠ³ΡΠ°ΠΌΠΌΡ Π³ΠΎΠ»ΠΎΠ²Π½ΠΎΠ³ΠΎ ΠΌΠΎΠ·Π³Π° Π΄Π»Ρ ΡΠΎΠΎΠ±ΡΠ΅ΡΡΠ² Ρ ΡΠ°Π·Π½ΡΠΌΠΈ Π²ΠΎΠ·ΠΌΠΎΠΆΠ½ΠΎΡΡΡΠΌΠΈ. ΠΠ»Ρ ΠΏΡΠ΅Π΄ΠΎΡΡΠ°Π²Π»Π΅Π½ΠΈΡ Π½Π°Π²ΠΈΠ³Π°ΡΠΈΠΎΠ½Π½ΠΎΠΉ ΡΠΈΡΡΠ΅ΠΌΠ΅ Π²ΡΠΏΠΎΠΌΠΎΠ³Π°ΡΠ΅Π»ΡΠ½ΡΡ
ΡΡΠ΅Π΄ΡΡΠ² ΡΠ²ΡΠ·ΠΈ Π² ΡΠ°Π±ΠΎΡΠ΅ ΠΏΡΠ΅Π΄Π»ΠΎΠΆΠ΅Π½ Π½Π°ΡΡΡΠ°ΠΈΠ²Π°Π΅ΠΌΡΠΉ ΠΏΡΠΎΡΠΎΠΊΠΎΠ», ΠΈΡΠΏΠΎΠ»ΡΠ·ΡΡΡΠΈΠΉ Π²ΡΠ·Π²Π°Π½Π½ΡΠ΅ ΠΌΡΡΠ»ΠΈΡΠ΅Π»ΡΠ½ΡΠ΅ ΠΏΠΎΡΠ΅Π½ΡΠΈΠ°Π»Ρ, ΡΡΠΎΠ±Ρ ΠΏΠΎΠΌΠΎΡΡ ΡΠΎΠΎΠ±ΡΠ΅ΡΡΠ²Π°ΠΌ Ρ ΡΠ°Π·Π½ΡΠΌΠΈ Π²ΠΎΠ·ΠΌΠΎΠΆΠ½ΠΎΡΡΡΠΌΠΈ. ΠΡΠ΅Π΄ΡΡΠ°Π²Π»Π΅Π½Ρ ΡΡΠ½ΠΊΡΠΈΠΈ, ΠΎΡΠ½ΠΎΠ²Π°Π½Π½ΡΠ΅ Π½Π° ΡΠΏΠ΅ΠΊΡΡΠ°Ρ
Π±ΠΎΠ»Π΅Π΅ Π²ΡΡΠΎΠΊΠΎΠ³ΠΎ ΠΏΠΎΡΡΠ΄ΠΊΠ°, Π΄Π»Ρ ΠΊΠ»Π°ΡΡΠΈΡΠΈΠΊΠ°ΡΠΈΠΈ ΡΠ΅ΠΌΠΈ ΠΎΡΠ½ΠΎΠ²Π½ΡΡ
Π·Π°Π΄Π°Ρ, ΡΠ°ΠΊΠΈΡ
ΠΊΠ°ΠΊ ΠΠΏΠ΅ΡΠ΅Π΄, ΠΠ»Π΅Π²ΠΎ, ΠΠΏΡΠ°Π²ΠΎ, ΠΠ°, ΠΠΠ’, ΠΠΎΠΌΠΎΡΡ ΠΈ Π Π°ΡΡΠ»Π°Π±Π»Π΅Π½ΠΈΠ΅, ΠΊΠΎΡΠΎΡΡΠ΅ ΠΌΠΎΠΆΠ½ΠΎ ΠΈΡΠΏΠΎΠ»ΡΠ·ΠΎΠ²Π°ΡΡ Π΄Π»Ρ ΡΠΏΡΠ°Π²Π»Π΅Π½ΠΈΡ ΠΊΡΠ΅ΡΠ»ΠΎΠΌ-ΡΠΎΠ±ΠΎΡΠΎΠΌ, Π° ΡΠ°ΠΊΠΆΠ΅ Π΄Π»Ρ ΡΠ²ΡΠ·ΠΈ Ρ ΠΈΡΠΏΠΎΠ»ΡΠ·ΠΎΠ²Π°Π½ΠΈΠ΅ΠΌ Π½Π΅ΠΎΠ±ΡΡΠ½ΠΎΠΉ ΠΏΠ°ΡΠ°Π΄ΠΈΠ³ΠΌΡ. ΠΡΠ΅Π΄Π»Π°Π³Π°Π΅ΠΌΠ°Ρ ΡΠΈΡΡΠ΅ΠΌΠ° Π·Π°ΠΏΠΈΡΡΠ²Π°Π΅Ρ Π²ΠΎΡΡΠΌΠΈΠΊΠ°Π½Π°Π»ΡΠ½ΡΠΉ Π±Π΅ΡΠΏΡΠΎΠ²ΠΎΠ΄Π½ΠΎΠΉ ΡΠΈΠ³Π½Π°Π» ΡΠ»Π΅ΠΊΡΡΠΎΡΠ½ΡΠ΅ΡΠ°Π»ΠΎΠ³ΡΠ°ΡΠΈΠΈ ΠΎΡ Π΄Π΅ΡΡΡΠΈ ΡΡΠ±ΡΠ΅ΠΊΡΠΎΠ², Π² ΡΠΎ Π²ΡΠ΅ΠΌΡ ΠΊΠ°ΠΊ ΡΡΠ±ΡΠ΅ΠΊΡ Π²ΠΎΡΠΏΡΠΈΠ½ΠΈΠΌΠ°Π» ΡΠ΅ΠΌΡ ΡΠ°Π·Π»ΠΈΡΠ½ΡΡ
Π·Π°Π΄Π°Ρ. ΠΠ°ΠΏΠΈΡΠ°Π½Π½ΡΠ΅ ΡΠΈΠ³Π½Π°Π»Ρ ΠΌΠΎΠ·Π³ΠΎΠ²ΡΡ
Π²ΠΎΠ»Π½ ΠΏΡΠ΅Π΄Π²Π°ΡΠΈΡΠ΅Π»ΡΠ½ΠΎ ΠΎΠ±ΡΠ°Π±Π°ΡΡΠ²Π°ΡΡΡΡ Π΄Π»Ρ ΡΠ΄Π°Π»Π΅Π½ΠΈΡ ΠΈΠ½ΡΠ΅ΡΡΠ΅ΡΠ΅Π½ΡΠΈΠΎΠ½Π½ΡΡ
Π²ΠΎΠ»Π½ ΠΈ ΡΠ΅Π³ΠΌΠ΅Π½ΡΠΈΡΡΡΡΡΡ Π½Π° ΡΠΈΠ³Π½Π°Π»Ρ ΡΠ΅ΡΡΠΈ ΡΠ°ΡΡΠΎΡΠ½ΡΡ
Π΄ΠΈΠ°ΠΏΠ°Π·ΠΎΠ½ΠΎΠ²: Π΄Π΅Π»ΡΡΠ°, ΡΠ΅ΡΠ°, Π°Π»ΡΡΠ°, Π±Π΅ΡΠ°, Π³Π°ΠΌΠΌΠ° 1-1 ΠΈ Π³Π°ΠΌΠΌΠ° 2. Π‘ΠΈΠ³Π½Π°Π»Ρ ΠΏΠΎΠ»ΠΎΡΡ ΡΠ°ΡΡΠΎΡ ΡΠ΅Π³ΠΌΠ΅Π½ΡΠΈΡΡΡΡΡΡ Π½Π° Π²ΡΠ±ΠΎΡΠΊΠΈ ΠΊΠ°Π΄ΡΠΎΠ² ΡΠ°Π²Π½ΠΎΠΉ Π΄Π»ΠΈΠ½Ρ ΠΈ ΠΈΡΠΏΠΎΠ»ΡΠ·ΡΡΡΡΡ Π΄Π»Ρ ΠΈΠ·Π²Π»Π΅ΡΠ΅Π½ΠΈΡ ΠΏΡΠΈΠ·Π½Π°ΠΊΠΎΠ² Ρ ΠΈΡΠΏΠΎΠ»ΡΠ·ΠΎΠ²Π°Π½ΠΈΠ΅ΠΌ ΠΎΡΠ΅Π½ΠΊΠΈ Π±ΠΈΡΠΏΠ΅ΠΊΡΡΠ°. ΠΡΠΎΠΌΠ΅ ΡΠΎΠ³ΠΎ, ΡΡΠ°ΡΠΈΡΡΠΈΡΠ΅ΡΠΊΠΈΠ΅ Ρ
Π°ΡΠ°ΠΊΡΠ΅ΡΠΈΡΡΠΈΠΊΠΈ, ΡΠ°ΠΊΠΈΠ΅ ΠΊΠ°ΠΊ ΡΡΠ΅Π΄Π½Π΅Π΅ Π·Π½Π°ΡΠ΅Π½ΠΈΠ΅ Π±ΠΈΡΠΏΠ΅ΠΊΡΡΠ°Π»ΡΠ½ΠΎΠΉ Π²Π΅Π»ΠΈΡΠΈΠ½Ρ ΠΈ ΡΠ½ΡΡΠΎΠΏΠΈΡ Ρ ΠΈΡΠΏΠΎΠ»ΡΠ·ΠΎΠ²Π°Π½ΠΈΠ΅ΠΌ ΠΎΠ±Π»Π°ΡΡΠΈ Π±ΠΈΡΠΏΠ΅ΠΊΡΡΠ°, ΠΈΠ·Π²Π»Π΅ΠΊΠ°ΡΡΡΡ ΠΈ ΡΠΎΡΠΌΠΈΡΡΡΡΡΡ ΠΊΠ°ΠΊ Π½Π°Π±ΠΎΡ Ρ
Π°ΡΠ°ΠΊΡΠ΅ΡΠΈΡΡΠΈΠΊ. ΠΠ·Π²Π»Π΅ΡΠ΅Π½Π½ΡΠ΅ Π½Π°Π±ΠΎΡΡ ΡΡΠ½ΠΊΡΠΈΠΉ ΠΏΡΠΎΡ
ΠΎΠ΄ΡΡ Π΄Π΅ΡΡΡΠΈΠΊΡΠ°ΡΠ½ΡΡ ΠΏΠ΅ΡΠ΅ΠΊΡΠ΅ΡΡΠ½ΡΡ ΠΏΡΠΎΠ²Π΅ΡΠΊΡ Ρ ΠΈΡΠΏΠΎΠ»ΡΠ·ΠΎΠ²Π°Π½ΠΈΠ΅ΠΌ ΠΊΠ»Π°ΡΡΠΈΡΠΈΠΊΠ°ΡΠΎΡΠ° ΠΌΠ½ΠΎΠ³ΠΎΡΠ»ΠΎΠΉΠ½ΠΎΠΉ Π½Π΅ΠΉΡΠΎΠ½Π½ΠΎΠΉ ΡΠ΅ΡΠΈ. Π Π΅Π·ΡΠ»ΡΡΠ°ΡΡ ΠΏΠΎΠΊΠ°Π·Π°Π»ΠΈ, ΡΡΠΎ ΡΠ½ΡΡΠΎΠΏΠΈΡ ΠΌΠΎΠ΄Π΅Π»ΠΈ ΠΊΠ»Π°ΡΡΠΈΡΠΈΠΊΠ°ΡΠΎΡΠ° Π½Π° ΠΎΡΠ½ΠΎΠ²Π΅ Ρ
Π°ΡΠ°ΠΊΡΠ΅ΡΠΈΡΡΠΈΠΊ Π±ΠΈΡΠΏΠ΅ΠΊΡΡΠ°Π»ΡΠ½ΠΎΠΉ Π²Π΅Π»ΠΈΡΠΈΠ½Ρ ΠΈΠΌΠ΅Π΅Ρ ΠΌΠ°ΠΊΡΠΈΠΌΠ°Π»ΡΠ½ΡΡ ΡΠΎΡΠ½ΠΎΡΡΡ ΠΊΠ»Π°ΡΡΠΈΡΠΈΠΊΠ°ΡΠΈΠΈ 84,71 %, Π° ΡΡΠ΅Π΄Π½Π΅Π΅ Π·Π½Π°ΡΠ΅Π½ΠΈΠ΅ ΠΌΠΎΠ΄Π΅Π»ΠΈ ΠΊΠ»Π°ΡΡΠΈΡΠΈΠΊΠ°ΡΠΎΡΠ° Π½Π° ΠΎΡΠ½ΠΎΠ²Π΅ Ρ
Π°ΡΠ°ΠΊΡΠ΅ΡΠΈΡΡΠΈΠΊ Π±ΠΈΡΠΏΠ΅ΠΊΡΡΠ°Π»ΡΠ½ΠΎΠΉ Π²Π΅Π»ΠΈΡΠΈΠ½Ρ β ΠΌΠΈΠ½ΠΈΠΌΠ°Π»ΡΠ½ΡΡ ΡΠΎΡΠ½ΠΎΡΡΡ ΠΊΠ»Π°ΡΡΠΈΡΠΈΠΊΠ°ΡΠΈΠΈ 68,52 %
Decentralized Harmonization Algorithm and Application to Functional Network Connectivity
Neuroimage data collected from multiple research institutions may incur additional source dependency, affecting the overall statistical power and leading to erroneous conclusions. This problem can be mitigated with data harmonization approaches. While open neuroimaging datasets are becoming more common, a substantial amount of data can still not be shared for various reasons. In addition, current approaches require moving all the data to a central location, which requires additional resources and creates redundant copies of the same datasets. To address these issues, we propose a decentralized harmonization approach called Decentralized ComBat that performs remote operations on the datasets separately without sharing individual subject data, ensuring a certain level of privacy and reducing regulatory hurdles. The study was conducted on harmonizing functional connectivity. Results showed similar performance as the centralized ComBat algorithm in a decentralized environment
Challenges and opportunities for the future of Brain-Computer Interface in neurorehabilitation
Brain-computer interfaces (BCIs) provide a unique technological solution to circumvent the damaged motor system. For neurorehabilitation, the BCI can be used to translate neural signals associated with movement intentions into tangible feedback for the patient, when they are unable to generate functional movement themselves. Clinical interest in BCI is growing rapidly, as it would facilitate rehabilitation to commence earlier following brain damage and provides options for patients who are unable to partake in traditional physical therapy. However, substantial challenges with existing BCI implementations have prevented its widespread adoption. Recent advances in knowledge and technology provide opportunities to facilitate a change, provided that researchers and clinicians using BCI agree on standardisation of guidelines for protocols and shared efforts to uncover mechanisms. We propose that addressing the speed and effectiveness of learning BCI control are priorities for the field, which may be improved by multimodal or multi-stage approaches harnessing more sensitive neuroimaging technologies in the early learning stages, before transitioning to more practical, mobile implementations. Clarification of the neural mechanisms that give rise to improvement in motor function is an essential next step towards justifying clinical use of BCI. In particular, quantifying the unknown contribution of non-motor mechanisms to motor recovery calls for more stringent control conditions in experimental work. Here we provide a contemporary viewpoint on the factors impeding the scalability of BCI. Further, we provide a future outlook for optimal design of the technology to best exploit its unique potential, and best practices for research and reporting of findings