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Restoring upper extremity function with brain-machine interfaces. INTERNATIONAL REVIEW OF NEUROBIOLOGY 2021; 159:153-186. [PMID: 34446245 DOI: 10.1016/bs.irn.2021.06.001] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 01/04/2023]
Abstract
One of the most exciting advances to emerge in neural interface technologies has been the development of real-time brain-machine interface (BMI) neuroprosthetic devices to restore upper extremity function. BMI neuroprostheses, made possible by synergistic advances in neural recording technologies, high-speed computation and signal processing, and neuroscience, have permitted the restoration of volitional movement to patients suffering the loss of upper-extremity function. In this chapter, we review the scientific and technological advances underlying these remarkable devices. After presenting an introduction to the current state of the field, we provide an accessible technical discussion of the two fundamental requirements of a successful neuroprosthesis: signal extraction from the brain and signal decoding that results in robust prosthetic control. We close with a presentation of emerging technologies that are likely to substantially advance the field.
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Olsen S, Zhang J, Liang KF, Lam M, Riaz U, Kao JC. An artificial intelligence that increases simulated brain-computer interface performance. J Neural Eng 2021; 18. [PMID: 33978599 DOI: 10.1088/1741-2552/abfaaa] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/16/2020] [Accepted: 04/22/2021] [Indexed: 12/14/2022]
Abstract
Objective.Brain-computer interfaces (BCIs) translate neural activity into control signals for assistive devices in order to help people with motor disabilities communicate effectively. In this work, we introduce a new BCI architecture that improves control of a BCI computer cursor to type on a virtual keyboard.Approach.Our BCI architecture incorporates an external artificial intelligence (AI) that beneficially augments the movement trajectories of the BCI. This AI-BCI leverages past user actions, at both long (100 s of seconds ago) and short (100 s of milliseconds ago) timescales, to modify the BCI's trajectories.Main results.We tested our AI-BCI in a closed-loop BCI simulator with nine human subjects performing a typing task. We demonstrate that our AI-BCI achieves: (1) categorically higher information communication rates, (2) quicker ballistic movements between targets, (3) improved precision control to 'dial in' on targets, and (4) more efficient movement trajectories. We further show that our AI-BCI increases performance across a wide control quality spectrum from poor to proficient control.Significance.This AI-BCI architecture, by increasing BCI performance across all key metrics evaluated, may increase the clinical viability of BCI systems.
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Affiliation(s)
- Sebastian Olsen
- Department of Electrical and Computer Engineering, University of California, Los Angeles, CA 90024, United States of America
| | - Jianwei Zhang
- Department of Electrical and Computer Engineering, University of California, Los Angeles, CA 90024, United States of America
| | - Ken-Fu Liang
- Department of Electrical and Computer Engineering, University of California, Los Angeles, CA 90024, United States of America
| | - Michelle Lam
- Department of Electrical and Computer Engineering, University of California, Los Angeles, CA 90024, United States of America
| | - Usama Riaz
- Department of Computer Science, University of California, Los Angeles, CA 90024, United States of America
| | - Jonathan C Kao
- Department of Electrical and Computer Engineering, University of California, Los Angeles, CA 90024, United States of America.,Neurosciences Program, University of California, Los Angeles, CA 90024, United States of America
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Nataraj SK, Pandiyan PM, Yaacob SB, Adom AHB. Intelligent robot chair with communication aid using TEP responses and higher order spectra band features. INFORMATICS 2021. [DOI: 10.37661/1816-0301-2020-17-4-92-103] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/24/2022] Open
Abstract
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 %.
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Kshirsagar GB, Londhe ND. Weighted Ensemble of Deep Convolution Neural Networks for Single-Trial Character Detection in Devanagari-Script-Based P300 Speller. IEEE Trans Cogn Dev Syst 2020. [DOI: 10.1109/tcds.2019.2942437] [Citation(s) in RCA: 12] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/11/2022]
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5
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Lee MH, Williamson J, Won DO, Fazli S, Lee SW. A High Performance Spelling System based on EEG-EOG Signals With Visual Feedback. IEEE Trans Neural Syst Rehabil Eng 2019; 26:1443-1459. [PMID: 29985154 DOI: 10.1109/tnsre.2018.2839116] [Citation(s) in RCA: 32] [Impact Index Per Article: 6.4] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/08/2022]
Abstract
In this paper, we propose a highly accurate and fast spelling system that employs multi-modal electroencephalography-electrooculography (EEG-EOG) signals and visual feedback technology. Over the last 20 years, various types of speller systems have been developed in brain-computer interface and EOG/eye-tracking research; however, these conventional systems have a tradeoff between the spelling accuracy (or decoding) and typing speed. Healthy users and physically challenged participants, in particular, may become exhausted quickly; thus, there is a need for a speller system with fast typing speed while retaining a high level of spelling accuracy. In this paper, we propose the first hybrid speller system that combines EEG and EOG signals with visual feedback technology so that the user and the speller system can act cooperatively for optimal decision-making. The proposed spelling system consists of a classic row-column event-related potential (ERP) speller, an EOG command detector, and visual feedback modules. First, the online ERP speller calculates classification probabilities for all candidate characters from the EEG epochs. Second, characters are sorted by their probability, and the characters with the highest probabilities are highlighted as visual feedback within the row-column spelling layout. Finally, the user can actively select the character as the target by generating an EOG command. The proposed system shows 97.6% spelling accuracy and an information transfer rate of 39.6 (±13.2) [bits/min] across 20 participants. In our extended experiment, we redesigned the visual feedback and minimized the number of channels (four channels) in order to enhance the speller performance and increase usability. Most importantly, a new weighted strategy resulted in 100% accuracy and a 57.8 (±23.6) [bits/min] information transfer rate across six participants. This paper demonstrates that the proposed system can provide a reliable communication channel for practical speller applications and may be used to supplement existing systems.
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6
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Speier W, Arnold C, Chandravadia N, Roberts D, Pendekanti S, Pouratian N. Improving P300 Spelling Rate using Language Models and Predictive Spelling. BRAIN-COMPUTER INTERFACES 2017; 5:13-22. [PMID: 30560145 DOI: 10.1080/2326263x.2017.1410418] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/18/2022]
Abstract
The P300 Speller Brain-Computer Interface (BCI) provides a means of communication for those suffering from advanced neuromuscular diseases such as amyotrophic lateral sclerosis (ALS). Recent literature has incorporated language-based modelling, which uses previously chosen characters and the structure of natural language to modify the interface and classifier. Two complementary methods of incorporating language models have previously been independently studied: predictive spelling uses language models to generate suggestions of complete words to allow for the selection of multiple characters simultaneously, and language model-based classifiers have used prior characters to create a prior probability distribution over the characters based on how likely they are to follow. In this study, we propose a combined method which extends a language-based classifier to generate prior probabilities for both individual characters and complete words. In order to gauge the efficiency of this new model, results across 12 healthy subjects were measured. Incorporating predictive spelling increased typing speed using the P300 speller, with an average increase of 15.5% in typing rate across subjects, demonstrating that language models can be effectively utilized to create full word suggestions for predictive spelling. When combining predictive spelling with language model classification, typing speed is significantly improved, resulting in better typing performance.
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Affiliation(s)
- William Speier
- Department of Neurosurgery, University of California, Los Angeles, USA.,Medical Imaging Informatics Group, University of California, Los Angeles, USA
| | - Corey Arnold
- Medical Imaging Informatics Group, University of California, Los Angeles, USA
| | - Nand Chandravadia
- Neuroscience Interdepartmental Program, University of California, Los Angeles, USA
| | - Dustin Roberts
- Department of Neurosurgery, University of California, Los Angeles, USA
| | - Shrita Pendekanti
- Neuroscience Interdepartmental Program, University of California, Los Angeles, USA
| | - Nader Pouratian
- Department of Neurosurgery, University of California, Los Angeles, USA.,Neuroscience Interdepartmental Program, University of California, Los Angeles, USA.,Department of Bioengineering, University of California, Los Angeles, USA.,Brain Research Institute, University of California, Los Angeles, USA
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7
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Cao L, Xia B, Maysam O, Li J, Xie H, Birbaumer N. A Synchronous Motor Imagery Based Neural Physiological Paradigm for Brain Computer Interface Speller. Front Hum Neurosci 2017; 11:274. [PMID: 28611611 PMCID: PMC5447015 DOI: 10.3389/fnhum.2017.00274] [Citation(s) in RCA: 18] [Impact Index Per Article: 2.6] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/05/2016] [Accepted: 05/09/2017] [Indexed: 11/13/2022] Open
Abstract
Brain Computer Interface (BCI) speller is a typical BCI-based application to help paralyzed patients express their thoughts. This paper proposed a novel motor imagery based BCI speller with Oct-o-spell paradigm for word input. Furthermore, an intelligent input method was used for improving the performance of the BCI speller. For the English word spelling experiment, we compared synchronous control with previous asynchronous control under the same experimental condition. There were no significant differences between these two control methods in the classification accuracy, information transmission rate (ITR) or letters per minute (LPM). And the accuracy rates of over 70% validated the feasibility for these two control strategies. It was indicated that MI-based synchronous control protocol was feasible for BCI speller. And the efficiency of the predictive text entry (PTE) mode was superior to that of the Non-PTE mode.
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Affiliation(s)
- Lei Cao
- Department of Computer Science, College of Information Engineering, Shanghai Maritime UniversityShanghai, China.,Institute of Medical Psychology and Behavioral Neurobiology, University of TuebingenTuebingen, Germany
| | - Bin Xia
- Department of Computer Science, College of Information Engineering, Shanghai Maritime UniversityShanghai, China.,Institute of Medical Psychology and Behavioral Neurobiology, University of TuebingenTuebingen, Germany
| | - Oladazimi Maysam
- Werner Reichardt, Center for Integrative Neuroscience (System Neurophysiology), University of TuebingenTuebingen, Germany
| | - Jie Li
- Department of Computer Science and Technology, Tongji UniversityShanghai, China
| | - Hong Xie
- Department of Computer Science, College of Information Engineering, Shanghai Maritime UniversityShanghai, China
| | - Niels Birbaumer
- Institute of Medical Psychology and Behavioral Neurobiology, University of TuebingenTuebingen, Germany.,IRCCS Fondazione Ospedale San CamilloVenezia, Italy
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8
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Speier W, Deshpande A, Cui L, Chandravadia N, Roberts D, Pouratian N. A comparison of stimulus types in online classification of the P300 speller using language models. PLoS One 2017; 12:e0175382. [PMID: 28406932 PMCID: PMC5391014 DOI: 10.1371/journal.pone.0175382] [Citation(s) in RCA: 23] [Impact Index Per Article: 3.3] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/06/2016] [Accepted: 03/06/2017] [Indexed: 11/18/2022] Open
Abstract
The P300 Speller is a common brain-computer interface communication system. There are many parallel lines of research underway to overcome the system's low signal to noise ratio and thereby improve performance, including using famous face stimuli and integrating language information into the classifier. While both have been shown separately to provide significant improvements, the two methods have not yet been implemented together to demonstrate that the improvements are complimentary. The goal of this study is therefore twofold. First, we aim to compare the famous faces stimulus paradigm with an existing alternative stimulus paradigm currently used in commercial systems (i.e., character inversion). Second, we test these methods with language model integration to assess whether different optimization approaches can be combined to further improve BCI communication. In offline analysis using a previously published particle filter method, famous faces stimuli yielded superior results to both standard and inverting stimuli. In online trials using the particle filter method, all 10 subjects achieved a higher selection rate when using the famous faces flashing paradigm than when using inverting flashes. The improvements achieved by these methods are therefore complementary and a combination yields superior results to either method implemented individually when tested in healthy subjects.
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Affiliation(s)
- William Speier
- Department of Neurosurgery, University of California, Los Angeles, Los Angeles, CA, United States of America
| | - Aniket Deshpande
- Department of Bioengineering, University of California, Los Angeles, Los Angeles, CA, United States of America
| | - Lucy Cui
- Neuroscience Interdepartmental Program, University of California, Los Angeles, Los Angeles, CA, United States of America
| | - Nand Chandravadia
- Neuroscience Interdepartmental Program, University of California, Los Angeles, Los Angeles, CA, United States of America
| | - Dustin Roberts
- Department of Neurosurgery, University of California, Los Angeles, Los Angeles, CA, United States of America
| | - Nader Pouratian
- Department of Neurosurgery, University of California, Los Angeles, Los Angeles, CA, United States of America
- Department of Bioengineering, University of California, Los Angeles, Los Angeles, CA, United States of America
- Neuroscience Interdepartmental Program, University of California, Los Angeles, Los Angeles, CA, United States of America
- Brain Research Institute, University of California, Los Angeles, Los Angeles, CA, United States of America
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9
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Mao X, Li M, Li W, Niu L, Xian B, Zeng M, Chen G. Progress in EEG-Based Brain Robot Interaction Systems. COMPUTATIONAL INTELLIGENCE AND NEUROSCIENCE 2017; 2017:1742862. [PMID: 28484488 PMCID: PMC5397651 DOI: 10.1155/2017/1742862] [Citation(s) in RCA: 39] [Impact Index Per Article: 5.6] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 12/28/2016] [Accepted: 03/21/2017] [Indexed: 11/17/2022]
Abstract
The most popular noninvasive Brain Robot Interaction (BRI) technology uses the electroencephalogram- (EEG-) based Brain Computer Interface (BCI), to serve as an additional communication channel, for robot control via brainwaves. This technology is promising for elderly or disabled patient assistance with daily life. The key issue of a BRI system is to identify human mental activities, by decoding brainwaves, acquired with an EEG device. Compared with other BCI applications, such as word speller, the development of these applications may be more challenging since control of robot systems via brainwaves must consider surrounding environment feedback in real-time, robot mechanical kinematics, and dynamics, as well as robot control architecture and behavior. This article reviews the major techniques needed for developing BRI systems. In this review article, we first briefly introduce the background and development of mind-controlled robot technologies. Second, we discuss the EEG-based brain signal models with respect to generating principles, evoking mechanisms, and experimental paradigms. Subsequently, we review in detail commonly used methods for decoding brain signals, namely, preprocessing, feature extraction, and feature classification, and summarize several typical application examples. Next, we describe a few BRI applications, including wheelchairs, manipulators, drones, and humanoid robots with respect to synchronous and asynchronous BCI-based techniques. Finally, we address some existing problems and challenges with future BRI techniques.
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Affiliation(s)
- Xiaoqian Mao
- School of Electrical Engineering and Automation, Tianjin University, Tianjin 300072, China
| | - Mengfan Li
- School of Electrical Engineering and Automation, Tianjin University, Tianjin 300072, China
| | - Wei Li
- School of Electrical Engineering and Automation, Tianjin University, Tianjin 300072, China
- Department of Computer & Electrical Engineering and Computer Science, California State University, Bakersfield, CA 93311, USA
- State Key Laboratory of Robotics, Shenyang Institute of Automation, Shenyang, Liaoning 110016, China
| | - Linwei Niu
- Department of Math and Computer Science, West Virginia State University, Institute, WV 25112, USA
| | - Bin Xian
- School of Electrical Engineering and Automation, Tianjin University, Tianjin 300072, China
| | - Ming Zeng
- School of Electrical Engineering and Automation, Tianjin University, Tianjin 300072, China
| | - Genshe Chen
- Intelligent Fusion Technology, Inc., Germantown, MD 20876, USA
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10
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Zhang J, Yin Z, Wang R. Design of an Adaptive Human-Machine System Based on Dynamical Pattern Recognition of Cognitive Task-Load. Front Neurosci 2017; 11:129. [PMID: 28367110 PMCID: PMC5355710 DOI: 10.3389/fnins.2017.00129] [Citation(s) in RCA: 5] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/02/2016] [Accepted: 03/02/2017] [Indexed: 11/25/2022] Open
Abstract
This paper developed a cognitive task-load (CTL) classification algorithm and allocation strategy to sustain the optimal operator CTL levels over time in safety-critical human-machine integrated systems. An adaptive human-machine system is designed based on a non-linear dynamic CTL classifier, which maps a set of electroencephalogram (EEG) and electrocardiogram (ECG) related features to a few CTL classes. The least-squares support vector machine (LSSVM) is used as dynamic pattern classifier. A series of electrophysiological and performance data acquisition experiments were performed on seven volunteer participants under a simulated process control task environment. The participant-specific dynamic LSSVM model is constructed to classify the instantaneous CTL into five classes at each time instant. The initial feature set, comprising 56 EEG and ECG related features, is reduced to a set of 12 salient features (including 11 EEG-related features) by using the locality preserving projection (LPP) technique. An overall correct classification rate of about 80% is achieved for the 5-class CTL classification problem. Then the predicted CTL is used to adaptively allocate the number of process control tasks between operator and computer-based controller. Simulation results showed that the overall performance of the human-machine system can be improved by using the adaptive automation strategy proposed.
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Affiliation(s)
- Jianhua Zhang
- Intelligent Systems Group, School of Information Science and Engineering, East China University of Science and TechnologyShanghai, China
| | - Zhong Yin
- School of Optical-Electrical and Computer Engineering, University of Shanghai for Science and TechnologyShanghai, China
| | - Rubin Wang
- Department of Mathematics, Institute of Cognitive Neurodynamics, School of Science, East China University of Science and TechnologyShanghai, China
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11
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Speier W, Chandravadia N, Roberts D, Pendekanti S, Pouratian N. Online BCI Typing using Language Model Classifiers by ALS Patients in their Homes. BRAIN-COMPUTER INTERFACES 2016; 4:114-121. [PMID: 29051907 DOI: 10.1080/2326263x.2016.1252143] [Citation(s) in RCA: 11] [Impact Index Per Article: 1.4] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/20/2022]
Abstract
The P300 speller is a common brain-computer interface system that can provide a means of communication for patients with amyotrophic lateral sclerosis (ALS). Recent studies have shown that incorporating language information in signal classification can improve system performance, but they have largely been tested on healthy volunteers in a laboratory setting. The goal of this study was to demonstrate the functionality of the P300 speller system with language models when used by ALS patients in their homes. Six ALS patients with functional ratings ranging from two to 28 participated in this study. All subjects had improved offline performance when using a language model and five subjects were able to type at least six characters per minute with over 84% accuracy in online sessions. The results of this study indicate that the improvements in performance using language models in the P300 speller translate into the ALS population, which could help to make it a viable assistive device.
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Affiliation(s)
- William Speier
- Department of Neurosurgery, University of California, Los Angeles, USA
| | - Nand Chandravadia
- Neuroscience Interdepartmental Program, University of California, Los Angeles, USA
| | - Dustin Roberts
- Department of Neurosurgery, University of California, Los Angeles, USA
| | - S Pendekanti
- Neuroscience Interdepartmental Program, University of California, Los Angeles, USA
| | - Nader Pouratian
- Department of Neurosurgery, University of California, Los Angeles, USA.,Neuroscience Interdepartmental Program, University of California, Los Angeles, USA.,Department of Bioengineering, University of California, Los Angeles, USA.,Brain Research Institute, University of California, Los Angeles, USA
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12
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Mainsah BO, Collins LM, Throckmorton CS. Using the detectability index to predict P300 speller performance. J Neural Eng 2016; 13:066007. [PMID: 27705956 DOI: 10.1088/1741-2560/13/6/066007] [Citation(s) in RCA: 9] [Impact Index Per Article: 1.1] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/11/2022]
Abstract
OBJECTIVE The P300 speller is a popular brain-computer interface (BCI) system that has been investigated as a potential communication alternative for individuals with severe neuromuscular limitations. To achieve acceptable accuracy levels for communication, the system requires repeated data measurements in a given signal condition to enhance the signal-to-noise ratio of elicited brain responses. These elicited brain responses, which are used as control signals, are embedded in noisy electroencephalography (EEG) data. The discriminability between target and non-target EEG responses defines a user's performance with the system. A previous P300 speller model has been proposed to estimate system accuracy given a certain amount of data collection. However, the approach was limited to a static stopping algorithm, i.e. averaging over a fixed number of measurements, and the row-column paradigm. A generalized method that is also applicable to dynamic stopping (DS) algorithms and other stimulus paradigms is desirable. APPROACH We developed a new probabilistic model-based approach to predicting BCI performance, where performance functions can be derived analytically or via Monte Carlo methods. Within this framework, we introduce a new model for the P300 speller with the Bayesian DS algorithm, by simplifying a multi-hypothesis to a binary hypothesis problem using the likelihood ratio test. Under a normality assumption, the performance functions for the Bayesian algorithm can be parameterized with the detectability index, a measure which quantifies the discriminability between target and non-target EEG responses. MAIN RESULTS Simulations with synthetic and empirical data provided initial verification of the proposed method of estimating performance with Bayesian DS using the detectability index. Analysis of results from previous online studies validated the proposed method. SIGNIFICANCE The proposed method could serve as a useful tool to initially assess BCI performance without extensive online testing, in order to estimate the amount of data required to achieve a desired accuracy level.
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Affiliation(s)
- B O Mainsah
- Duke University, Department of Electrical and Computer Engineering, Durham, NC, USA
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13
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Kim K, Lim SH, Lee J, Kang WS, Moon C, Choi JW. Joint Maximum Likelihood Time Delay Estimation of Unknown Event-Related Potential Signals for EEG Sensor Signal Quality Enhancement. SENSORS 2016; 16:s16060891. [PMID: 27322267 PMCID: PMC4934317 DOI: 10.3390/s16060891] [Citation(s) in RCA: 5] [Impact Index Per Article: 0.6] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 02/01/2016] [Revised: 06/03/2016] [Accepted: 06/09/2016] [Indexed: 12/02/2022]
Abstract
Electroencephalograms (EEGs) measure a brain signal that contains abundant information about the human brain function and health. For this reason, recent clinical brain research and brain computer interface (BCI) studies use EEG signals in many applications. Due to the significant noise in EEG traces, signal processing to enhance the signal to noise power ratio (SNR) is necessary for EEG analysis, especially for non-invasive EEG. A typical method to improve the SNR is averaging many trials of event related potential (ERP) signal that represents a brain’s response to a particular stimulus or a task. The averaging, however, is very sensitive to variable delays. In this study, we propose two time delay estimation (TDE) schemes based on a joint maximum likelihood (ML) criterion to compensate the uncertain delays which may be different in each trial. We evaluate the performance for different types of signals such as random, deterministic, and real EEG signals. The results show that the proposed schemes provide better performance than other conventional schemes employing averaged signal as a reference, e.g., up to 4 dB gain at the expected delay error of 10°.
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Affiliation(s)
- Kyungsoo Kim
- Department of Information & Communication Engineering, Daegu Gyeongbuk Institute of Science and Technology, Daegu 771-813, Korea.
| | - Sung-Ho Lim
- Department of Information & Communication Engineering, Daegu Gyeongbuk Institute of Science and Technology, Daegu 771-813, Korea.
| | - Jaeseok Lee
- Department of Information & Communication Engineering, Daegu Gyeongbuk Institute of Science and Technology, Daegu 771-813, Korea.
| | - Won-Seok Kang
- Wellness Convergence Research Center, Daegu Gyeongbuk Institute of Science and Technology (DGIST), Daegu 771-813, Korea.
| | - Cheil Moon
- Department of Brain & Cognitive Sciences, Daegu Gyeongbuk Institute of Science and Technology (DGIST), Daegu 771-813, Korea.
| | - Ji-Woong Choi
- Department of Information & Communication Engineering, Daegu Gyeongbuk Institute of Science and Technology, Daegu 771-813, Korea.
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14
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Speier W, Arnold C, Pouratian N. Integrating language models into classifiers for BCI communication: a review. J Neural Eng 2016; 13:031002. [PMID: 27153565 DOI: 10.1088/1741-2560/13/3/031002] [Citation(s) in RCA: 32] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/11/2022]
Abstract
OBJECTIVE The present review systematically examines the integration of language models to improve classifier performance in brain-computer interface (BCI) communication systems. APPROACH The domain of natural language has been studied extensively in linguistics and has been used in the natural language processing field in applications including information extraction, machine translation, and speech recognition. While these methods have been used for years in traditional augmentative and assistive communication devices, information about the output domain has largely been ignored in BCI communication systems. Over the last few years, BCI communication systems have started to leverage this information through the inclusion of language models. MAIN RESULTS Although this movement began only recently, studies have already shown the potential of language integration in BCI communication and it has become a growing field in BCI research. BCI communication systems using language models in their classifiers have progressed down several parallel paths, including: word completion; signal classification; integration of process models; dynamic stopping; unsupervised learning; error correction; and evaluation. SIGNIFICANCE Each of these methods have shown significant progress, but have largely been addressed separately. Combining these methods could use the full potential of language model, yielding further performance improvements. This integration should be a priority as the field works to create a BCI system that meets the needs of the amyotrophic lateral sclerosis population.
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Affiliation(s)
- W Speier
- Department of Neurosurgery, University of California, Los Angeles, CA 90095, USA. Medical Imaging Informatics Group, University of California, Los Angeles, CA 90095, USA
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15
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Speier W, Arnold CW, Deshpande A, Knall J, Pouratian N. Incorporating advanced language models into the P300 speller using particle filtering. J Neural Eng 2015; 12:046018. [PMID: 26061188 DOI: 10.1088/1741-2560/12/4/046018] [Citation(s) in RCA: 12] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/12/2022]
Abstract
OBJECTIVE The P300 speller is a common brain-computer interface (BCI) application designed to communicate language by detecting event related potentials in a subject's electroencephalogram signal. Information about the structure of natural language can be valuable for BCI communication, but attempts to use this information have thus far been limited to rudimentary n-gram models. While more sophisticated language models are prevalent in natural language processing literature, current BCI analysis methods based on dynamic programming cannot handle their complexity. APPROACH Sampling methods can overcome this complexity by estimating the posterior distribution without searching the entire state space of the model. In this study, we implement sequential importance resampling, a commonly used particle filtering (PF) algorithm, to integrate a probabilistic automaton language model. MAIN RESULT This method was first evaluated offline on a dataset of 15 healthy subjects, which showed significant increases in speed and accuracy when compared to standard classification methods as well as a recently published approach using a hidden Markov model (HMM). An online pilot study verified these results as the average speed and accuracy achieved using the PF method was significantly higher than that using the HMM method. SIGNIFICANCE These findings strongly support the integration of domain-specific knowledge into BCI classification to improve system performance.
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Affiliation(s)
- W Speier
- Department of Bioengineering, University of California, Los Angeles, CA 90095, USA
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Saa JFD, de Pesters A, McFarland D, Çetin M. Word-level language modeling for P300 spellers based on discriminative graphical models. J Neural Eng 2015; 12:026007. [PMID: 25686293 PMCID: PMC4955587 DOI: 10.1088/1741-2560/12/2/026007] [Citation(s) in RCA: 7] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/12/2022]
Abstract
OBJECTIVE In this work we propose a probabilistic graphical model framework that uses language priors at the level of words as a mechanism to increase the performance of P300-based spellers. APPROACH This paper is concerned with brain-computer interfaces based on P300 spellers. Motivated by P300 spelling scenarios involving communication based on a limited vocabulary, we propose a probabilistic graphical model framework and an associated classification algorithm that uses learned statistical models of language at the level of words. Exploiting such high-level contextual information helps reduce the error rate of the speller. MAIN RESULTS Our experimental results demonstrate that the proposed approach offers several advantages over existing methods. Most importantly, it increases the classification accuracy while reducing the number of times the letters need to be flashed, increasing the communication rate of the system. SIGNIFICANCE The proposed approach models all the variables in the P300 speller in a unified framework and has the capability to correct errors in previous letters in a word, given the data for the current one. The structure of the model we propose allows the use of efficient inference algorithms, which in turn makes it possible to use this approach in real-time applications.
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Affiliation(s)
- Jaime F Delgado Saa
- Signal Proc. Info. Syst. Lab, Sabanci University, Istanbul, Turkey
- Robotics & Intelligent Syst. Lab, Universidad del Norte, Barranquilla, Colombia
| | | | | | - Müjdat Çetin
- Signal Proc. Info. Syst. Lab, Sabanci University, Istanbul, Turkey
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Mainsah BO, Collins LM, Colwell KA, Sellers EW, Ryan DB, Caves K, Throckmorton CS. Increasing BCI communication rates with dynamic stopping towards more practical use: an ALS study. J Neural Eng 2015; 12:016013. [PMID: 25588137 DOI: 10.1088/1741-2560/12/1/016013] [Citation(s) in RCA: 42] [Impact Index Per Article: 4.7] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/11/2022]
Abstract
OBJECTIVE The P300 speller is a brain-computer interface (BCI) that can possibly restore communication abilities to individuals with severe neuromuscular disabilities, such as amyotrophic lateral sclerosis (ALS), by exploiting elicited brain signals in electroencephalography (EEG) data. However, accurate spelling with BCIs is slow due to the need to average data over multiple trials to increase the signal-to-noise ratio (SNR) of the elicited brain signals. Probabilistic approaches to dynamically control data collection have shown improved performance in non-disabled populations; however, validation of these approaches in a target BCI user population has not occurred. APPROACH We have developed a data-driven algorithm for the P300 speller based on Bayesian inference that improves spelling time by adaptively selecting the number of trials based on the acute SNR of a user's EEG data. We further enhanced the algorithm by incorporating information about the user's language. In this current study, we test and validate the algorithms online in a target BCI user population, by comparing the performance of the dynamic stopping (DS) (or early stopping) algorithms against the current state-of-the-art method, static data collection, where the amount of data collected is fixed prior to online operation. MAIN RESULTS Results from online testing of the DS algorithms in participants with ALS demonstrate a significant increase in communication rate as measured in bits/min (100-300%), and theoretical bit rate (100-550%), while maintaining selection accuracy. Participants also overwhelmingly preferred the DS algorithms. SIGNIFICANCE We have developed a viable BCI algorithm that has been tested in a target BCI population which has the potential for translation to improve BCI speller performance towards more practical use for communication.
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Affiliation(s)
- B O Mainsah
- Duke University, Department of Electrical and Computer Engineering, USA
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Khorasani A, Daliri MR. HMM for Classification of Parkinson’s Disease Based on the Raw Gait Data. J Med Syst 2014; 38:147. [DOI: 10.1007/s10916-014-0147-5] [Citation(s) in RCA: 54] [Impact Index Per Article: 5.4] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/14/2014] [Accepted: 10/22/2014] [Indexed: 12/01/2022]
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Bielza C, Larrañaga P. Bayesian networks in neuroscience: a survey. Front Comput Neurosci 2014; 8:131. [PMID: 25360109 PMCID: PMC4199264 DOI: 10.3389/fncom.2014.00131] [Citation(s) in RCA: 48] [Impact Index Per Article: 4.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/30/2014] [Accepted: 09/26/2014] [Indexed: 12/29/2022] Open
Abstract
Bayesian networks are a type of probabilistic graphical models lie at the intersection between statistics and machine learning. They have been shown to be powerful tools to encode dependence relationships among the variables of a domain under uncertainty. Thanks to their generality, Bayesian networks can accommodate continuous and discrete variables, as well as temporal processes. In this paper we review Bayesian networks and how they can be learned automatically from data by means of structure learning algorithms. Also, we examine how a user can take advantage of these networks for reasoning by exact or approximate inference algorithms that propagate the given evidence through the graphical structure. Despite their applicability in many fields, they have been little used in neuroscience, where they have focused on specific problems, like functional connectivity analysis from neuroimaging data. Here we survey key research in neuroscience where Bayesian networks have been used with different aims: discover associations between variables, perform probabilistic reasoning over the model, and classify new observations with and without supervision. The networks are learned from data of any kind-morphological, electrophysiological, -omics and neuroimaging-, thereby broadening the scope-molecular, cellular, structural, functional, cognitive and medical- of the brain aspects to be studied.
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Affiliation(s)
- Concha Bielza
- *Correspondence: Concha Bielza, Departamento de Inteligencia Artificial, Universidad Politecnica de Madrid, Campus de Montegancedo, Boadilla del Monte, 28660 Madrid, Spain e-mail:
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Speier W, Deshpande A, Pouratian N. A method for optimizing EEG electrode number and configuration for signal acquisition in P300 speller systems. Clin Neurophysiol 2014; 126:1171-1177. [PMID: 25316166 DOI: 10.1016/j.clinph.2014.09.021] [Citation(s) in RCA: 15] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/31/2014] [Revised: 09/04/2014] [Accepted: 09/22/2014] [Indexed: 10/24/2022]
Abstract
OBJECTIVE The P300 speller is intended to restore communication to patients with advanced neuromuscular disorders, but clinical implementation may be hindered by several factors, including system setup, burden, and cost. Our goal was to develop a method that can overcome these barriers by optimizing EEG electrode number and placement for P300 studies within a population of subjects. METHODS A Gibbs sampling method was developed to find the optimal electrode configuration given a set of P300 speller data. The method was tested on a set of data from 15 healthy subjects using an established 32-electrode pattern. Resulting electrode configurations were then validated using online prospective testing with a naïve Bayes classifier in 15 additional healthy subjects. RESULTS The method yielded a set of four posterior electrodes (PO₈, PO₇, POZ, CPZ), which produced results that are likely sufficient to be clinically effective. In online prospective validation testing, no significant difference was found between subjects' performances using the reduced and the full electrode configurations. CONCLUSIONS The proposed method can find reduced sets of electrodes within a subject population without reducing performance. SIGNIFICANCE Reducing the number of channels may reduce costs, set-up time, signal bandwidth, and computation requirements for practical online P300 speller implementation.
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Affiliation(s)
- William Speier
- University of California, Los Angeles, Department of Bioengineering, David Geffen School of Medicine at UCLA, Los Angeles, CA, USA; University of California, Los Angeles, Medical Imaging Informatics Group, David Geffen School of Medicine at UCLA, Los Angeles, CA, USA
| | - Aniket Deshpande
- University of California, Los Angeles, Department of Bioengineering, David Geffen School of Medicine at UCLA, Los Angeles, CA, USA
| | - Nader Pouratian
- University of California, Los Angeles, Department of Bioengineering, David Geffen School of Medicine at UCLA, Los Angeles, CA, USA; University of California, Los Angeles, Department of Neurosurgery, David Geffen School of Medicine at UCLA, Los Angeles, CA, USA; University of California, Los Angeles, Interdepartmental Program in Neuroscience, David Geffen School of Medicine at UCLA, Los Angeles, CA, USA; University of California, Los Angeles, Brain Research Institute, David Geffen School of Medicine at UCLA, Los Angeles, CA, USA.
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