scholarly journals Selection of Essential Neural Activity Timesteps for Intracortical Brain–Computer Interface Based on Recurrent Neural Network

Sensors ◽  
2021 ◽  
Vol 21 (19) ◽  
pp. 6372
Author(s):  
Shih-Hung Yang ◽  
Jyun-We Huang ◽  
Chun-Jui Huang ◽  
Po-Hsiung Chiu ◽  
Hsin-Yi Lai ◽  
...  
Keyword(s):  
Neural Activity ◽  
Computation Time ◽  
Arm Movement ◽  
Neural Decoding ◽  
Temporal Attention ◽  
Response Dynamics ◽  
Computer Interfaces ◽  
Brain Signals ◽  
Control Devices ◽  
Selection Of

Intracortical brain–computer interfaces (iBCIs) translate neural activity into control commands, thereby allowing paralyzed persons to control devices via their brain signals. Recurrent neural networks (RNNs) are widely used as neural decoders because they can learn neural response dynamics from continuous neural activity. Nevertheless, excessively long or short input neural activity for an RNN may decrease its decoding performance. Based on the temporal attention module exploiting relations in features over time, we propose a temporal attention-aware timestep selection (TTS) method that improves the interpretability of the salience of each timestep in an input neural activity. Furthermore, TTS determines the appropriate input neural activity length for accurate neural decoding. Experimental results show that the proposed TTS efficiently selects 28 essential timesteps for RNN-based neural decoders, outperforming state-of-the-art neural decoders on two nonhuman primate datasets (R2=0.76±0.05 for monkey Indy and CC=0.91±0.01 for monkey N). In addition, it reduces the computation time for offline training (reducing 5–12%) and online prediction (reducing 16–18%). When visualizing the attention mechanism in TTS, the preparatory neural activity is consecutively highlighted during arm movement, and the most recent neural activity is highlighted during the resting state in nonhuman primates. Selecting only a few essential timesteps for an RNN-based neural decoder provides sufficient decoding performance and requires only a short computation time.

Methods for Restricting Maximum Exposure Rate in Computerized Adaptative Testing

Methodology ◽  
2007 ◽  
Vol 3 (1) ◽  
pp. 14-23 ◽  
Author(s):  
Juan Ramon Barrada ◽  
Julio Olea ◽  
Vicente Ponsoda
Keyword(s):  
Measurement Accuracy ◽  
Computerized Adaptive Testing ◽  
Computation Time ◽  
Adaptive Testing ◽  
Exposure Rate ◽  
Control Parameters ◽  
The Impact ◽  
Two Alternatives ◽  
Selection Of ◽  
Maximum Exposure

Abstract. The Sympson-Hetter (1985) method provides a means of controlling maximum exposure rate of items in Computerized Adaptive Testing. Through a series of simulations, control parameters are set that mark the probability of administration of an item on being selected. This method presents two main problems: it requires a long computation time for calculating the parameters and the maximum exposure rate is slightly above the fixed limit. Van der Linden (2003) presented two alternatives which appear to solve both of the problems. The impact of these methods in the measurement accuracy has not been tested yet. We show how these methods over-restrict the exposure of some highly discriminating items and, thus, the accuracy is decreased. It also shown that, when the desired maximum exposure rate is near the minimum possible value, these methods offer an empirical maximum exposure rate clearly above the goal. A new method, based on the initial estimation of the probability of administration and the probability of selection of the items with the restricted method ( Revuelta & Ponsoda, 1998 ), is presented in this paper. It can be used with the Sympson-Hetter method and with the two van der Linden's methods. This option, when used with Sympson-Hetter, speeds the convergence of the control parameters without decreasing the accuracy.

Selection of Passive Inflow Control Devices Based on Dynamic Weight Fuzzy Evaluation

2014 ◽  
Author(s):  
Quanshu Zeng ◽  
Zhiming Wang ◽  
Xiaoqiu Wang ◽  
Yiwei Li ◽  
Weilin Zou ◽  
...  
Keyword(s):  
Fuzzy Evaluation ◽  
Dynamic Weight ◽  
Control Devices ◽  
Selection Of

scholarly journals An Introductory Tutorial on Brain–Computer Interfaces and Their Applications

Electronics ◽  
2021 ◽  
Vol 10 (5) ◽  
pp. 560
Author(s):  
Andrea Bonci ◽  
Simone Fiori ◽  
Hiroshi Higashi ◽  
Toshihisa Tanaka ◽  
Federica Verdini
Keyword(s):  
Biomedical Engineering ◽  
Data Privacy ◽  
Lie Detection ◽  
Human Cognition ◽  
Brain Computer Interfaces ◽  
Computer Interfaces ◽  
Brain Signals ◽  
Ethical And Legal Issues ◽  
And Performance ◽  
Brain Computer Interfacing

The prospect and potentiality of interfacing minds with machines has long captured human imagination. Recent advances in biomedical engineering, computer science, and neuroscience are making brain–computer interfaces a reality, paving the way to restoring and potentially augmenting human physical and mental capabilities. Applications of brain–computer interfaces are being explored in applications as diverse as security, lie detection, alertness monitoring, gaming, education, art, and human cognition augmentation. The present tutorial aims to survey the principal features and challenges of brain–computer interfaces (such as reliable acquisition of brain signals, filtering and processing of the acquired brainwaves, ethical and legal issues related to brain–computer interface (BCI), data privacy, and performance assessment) with special emphasis to biomedical engineering and automation engineering applications. The content of this paper is aimed at students, researchers, and practitioners to glimpse the multifaceted world of brain–computer interfacing.

scholarly journals Brain–Computer Interface (BCI) Control of a Virtual Assistant in a Smartphone to Manage Messaging Applications

Sensors ◽  
2021 ◽  
Vol 21 (11) ◽  
pp. 3716
Author(s):  
Francisco Velasco-Álvarez ◽  
Álvaro Fernández-Rodríguez ◽  
Francisco-Javier Vizcaíno-Martín ◽  
Antonio Díaz-Estrella ◽  
Ricardo Ron-Angevin
Keyword(s):  
Free Text ◽  
Short Message ◽  
Negative Consequences ◽  
Computer Interfaces ◽  
Brain Signals ◽  
Message Service ◽  
Performance Results ◽  
And Control ◽  
E Mail ◽  
Visual P300

Brain–computer interfaces (BCI) are a type of assistive technology that uses the brain signals of users to establish a communication and control channel between them and an external device. BCI systems may be a suitable tool to restore communication skills in severely motor-disabled patients, as BCI do not rely on muscular control. The loss of communication is one of the most negative consequences reported by such patients. This paper presents a BCI system focused on the control of four mainstream messaging applications running in a smartphone: WhatsApp, Telegram, e-mail and short message service (SMS). The control of the BCI is achieved through the well-known visual P300 row-column paradigm (RCP), allowing the user to select control commands as well as spelling characters. For the control of the smartphone, the system sends synthesized voice commands that are interpreted by a virtual assistant running in the smartphone. Four tasks related to the four mentioned messaging services were tested with 15 healthy volunteers, most of whom were able to accomplish the tasks, which included sending free text e-mails to an address proposed by the subjects themselves. The online performance results obtained, as well as the results of subjective questionnaires, support the viability of the proposed system.

scholarly journals Amino Acids as Gustatory Stimuli in the Rat

1962 ◽  
Vol 45 (4) ◽  
pp. 681-701 ◽  
Author(s):  
Bruce P. Halpern ◽  
Rudy A. Bernard ◽  
Morley R. Kare
Keyword(s):  
Amino Acids ◽  
Neural Activity ◽  
Receptor Site ◽  
Chorda Tympani ◽  
Chorda Tympani Nerve ◽  
High Concentration ◽  
Low Concentration ◽  
Common Receptor ◽  
Amino Acid Solutions ◽  
Selection Of

Neural activity in intact chorda tympani nerve of rats was studied with an electronic summator. Neural activity increased when amino acid solutions 0.01 M or above passed over the tongue. Response magnitude, at concentrations close to solubility limits for the amino acids tested, was: DL-methionine < DL-tryptophan < DL-valine < DL-alanine < glycine < 0.1 M NaCl. Maximum response magnitudes to 1 M D-, and 1.2 M DL-alanine, and 1.5 M glycine developed in 1 to 3 minutes. Following such stimulation, a 63 per cent reduction in response to 0.1 M NaCl occurred 60 minutes after the first stimulation (medians). The depression was still present 20 hours later. Responses to glycine and alanine were not depressed. Amino acids vs. water preferences were investigated. With ascending concentration sequences, rats selected low concentration DL- and L-alanine and glycine; accepted D-, L-, and DL-tryptophan and low concentration DL-methionine; and rejected high concentration glycine, DL-alanine, and DL-methionine. Descending sequences showed depressed and delayed selection of glycine and DL-alanine, and DL-methionine and D- and L-tryptophan rejection. Both groups rejected DL-valine. It is concluded that glycine and alanine receptor effects differ from those of NaCl, but that all three compounds may affect a common receptor site. Prior exposure to amino acids may modify subsequent neural and/or behavioral responses.

scholarly journals EEG Waveform Analysis of P300 ERP with Applications to Brain Computer Interfaces

2018 ◽  
Vol 8 (11) ◽  
pp. 199 ◽  
Author(s):  
Rodrigo Ramele ◽  
Ana Villar ◽  
Juan Santos
Keyword(s):  
Brain Computer Interface ◽  
Waveform Analysis ◽  
Computer Interface ◽  
Clinical Tool ◽  
Non Invasive ◽  
Computer Interfaces ◽  
Brain Signals ◽  
Public Dataset ◽  
Imaging Sensor ◽  
Clinical Eeg

The Electroencephalography (EEG) is not just a mere clinical tool anymore. It has become the de-facto mobile, portable, non-invasive brain imaging sensor to harness brain information in real time. It is now being used to translate or decode brain signals, to diagnose diseases or to implement Brain Computer Interface (BCI) devices. The automatic decoding is mainly implemented by using quantitative algorithms to detect the cloaked information buried in the signal. However, clinical EEG is based intensively on waveforms and the structure of signal plots. Hence, the purpose of this work is to establish a bridge to fill this gap by reviewing and describing the procedures that have been used to detect patterns in the electroencephalographic waveforms, benchmarking them on a controlled pseudo-real dataset of a P300-Based BCI Speller and verifying their performance on a public dataset of a BCI Competition.

Shape Memory Alloy Actuatable Woven Neural Probes

2018 ◽  
Author(s):  
Gabriella Shull ◽  
Jay Jia Hu ◽  
Justin Buschnyj ◽  
Henry Koon ◽  
Julianna Abel ◽  
...  
Keyword(s):  
Shape Memory Alloy ◽  
Shape Memory ◽  
Neural Activity ◽  
Inflammatory Responses ◽  
Neural Probes ◽  
Thermally Induced ◽  
Tissue Phantom ◽  
Computer Interfaces ◽  
Ordered Array ◽  
The Brain

The ability to sense neural activity using electrodes has allowed scientists to use this information to temporarily restore movement in paralyzed individuals using brain-computer interfaces (BCI). However, current electrodes do not provide chronic recording of the brain due to the inflammatory response of the immune system caused by the large (∼ 20–80 μm) size of the shanks, and the mechanical mismatch of the shanks relative to the brain. Electrode designs are evolving to use small (< 15 μm) flexible neural probes to minimize inflammatory responses and enable chronic use. However, their flexibility limits the scalability — it is challenging to assemble 3D arrays of such electrodes, to insert the arrays of flexible neural probes into the brain without buckling, and to uniformly distribute them into large areas of the brain. Thus, we created Shape Memory Alloy (SMA) actuated Woven Neural Probes (WNPs). A linear array of 32 flexible insulated microwires were interwoven with SMA wires resulting in an ordered array of parallel electrodes. SMA WNPs were shaped to an initial constricted profile for reliable insertion into a tissue phantom. Following insertion, the SMA wires were used as actuators to unravel the constricted WNP to distribute electrodes across large volumes. We demonstrated that the WNPs could be inserted into the brain without buckling and record neural activity. In separate experiments, we showed that the SMA could mechanically distribute the WNPs via thermally induced actuation. This work thus highlights the potential of actuatable WNPs to be used as a platform for neural recording.

scholarly journals Sequence Transfer Learning for Neural Decoding

2017 ◽  
Author(s):  
Venkatesh Elango ◽  
Aashish N Patel ◽  
Kai J Miller ◽  
Vikash Gilja
Keyword(s):  
Transfer Learning ◽  
Affine Transformation ◽  
Linear Models ◽  
Short Term Memory ◽  
Training Data ◽  
Neural Decoding ◽  
Electrode Arrays ◽  
Neural Data ◽  
Computer Interfaces ◽  
Finger Flexion

AbstractA fundamental challenge in designing brain-computer interfaces (BCIs) is decoding behavior from time-varying neural oscillations. in typical applications, decoders are constructed for individual subjects and with limited data leading to restrictions on the types of models that can be utilized. currently, the best performing decoders are typically linear models capable of utilizing rigid timing constraints with limited training data. Here we demonstrate the use of Long Short-Term Memory (LSTM) networks to take advantage of the temporal information present in sequential neural data collected from subjects implanted with electrocorticographic (ECoG) electrode arrays performing a finger flexion task. our constructed models are capable of achieving accuracies that are comparable to existing techniques while also being robust to variation in sample data size. Moreover, we utilize the LSTM networks and an affine transformation layer to construct a novel architecture for transfer learning. We demonstrate that in scenarios where only the affine transform is learned for a new subject, it is possible to achieve results comparable to existing state-of-the-art techniques. The notable advantage is the increased stability of the model during training on novel subjects. Relaxing the constraint of only training the affine transformation, we establish our model as capable of exceeding performance of current models across all training data sizes. Overall, this work demonstrates that LSTMS are a versatile model that can accurately capture temporal patterns in neural data and can provide a foundation for transfer learning in neural decoding.

scholarly journals The current state of electrocorticography-based brain–computer interfaces

2020 ◽  
Vol 49 (1) ◽  
pp. E2 ◽  
Author(s):  
Kai J. Miller ◽  
Dora Hermes ◽  
Nathan P. Staff
Keyword(s):  
Functional Limitations ◽  
Brain Computer Interfaces ◽  
Brain Surface ◽  
Computer Interfaces ◽  
Brain Signals ◽  
Current State ◽  
Temporal And Spatial ◽  
Direct Feedback ◽  
The Brain ◽  
Lateral Sclerosis

Brain–computer interfaces (BCIs) provide a way for the brain to interface directly with a computer. Many different brain signals can be used to control a device, varying in ease of recording, reliability, stability, temporal and spatial resolution, and noise. Electrocorticography (ECoG) electrodes provide a highly reliable signal from the human brain surface, and these signals have been used to decode movements, vision, and speech. ECoG-based BCIs are being developed to provide increased options for treatment and assistive devices for patients who have functional limitations. Decoding ECoG signals in real time provides direct feedback to the patient and can be used to control a cursor on a computer or an exoskeleton. In this review, the authors describe the current state of ECoG-based BCIs that are approaching clinical viability for restoring lost communication and motor function in patients with amyotrophic lateral sclerosis or tetraplegia. These studies provide a proof of principle and the possibility that ECoG-based BCI technology may also be useful in the future for assisting in the cortical rehabilitation of patients who have suffered a stroke.

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