A Cognitive Brain-Computer Interface for Patients with Amyotrophic Lateral Sclerosis

Hohmann, Matthias R.; Fomina, Tatiana; Jayaram, Vinay; Widmann, Natalie; Förster, Christian; Hagen, Jennifer Müller vom; Synofzik, Matthis; Schölkopf, Bernhard; Schöls, Ludger; Grosse-Wentrup, Moritz

doi:10.1109/smc.2015.553

Cited by 3 publications

(6 citation statements)

References 26 publications

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“…However, such spatial-spectral EEG features may not be discriminative for patients with ALS due to the fact that the neurons in the motor cortex have been degenerated in ALS [ 20 ], leading to the impairment in the ability to modulate the SMRs [ 21 , 22 ]. Accordingly, although the motor imagery is more feasible for late-stage ALS patients to control BCIs, the mission of designing high-accuracy motor-imagery BCIs is very challenging [ 10 , 23 ].…”

Section: Introductionmentioning

confidence: 99%

Motor Imagery EEG Classification for Patients with Amyotrophic Lateral Sclerosis Using Fractal Dimension and Fisher’s Criterion-Based Channel Selection

Liu

Huang

2017

Sensors

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Motor imagery is based on the volitional modulation of sensorimotor rhythms (SMRs); however, the sensorimotor processes in patients with amyotrophic lateral sclerosis (ALS) are impaired, leading to degenerated motor imagery ability. Thus, motor imagery classification in ALS patients has been considered challenging in the brain–computer interface (BCI) community. In this study, we address this critical issue by introducing the Grassberger–Procaccia and Higuchi’s methods to estimate the fractal dimensions (GPFD and HFD, respectively) of the electroencephalography (EEG) signals from ALS patients. Moreover, a Fisher’s criterion-based channel selection strategy is proposed to automatically determine the best patient-dependent channel configuration from 30 EEG recording sites. An EEG data collection paradigm is designed to collect the EEG signal of resting state and the imagination of three movements, including right hand grasping (RH), left hand grasping (LH), and left foot stepping (LF). Five late-stage ALS patients without receiving any SMR training participated in this study. Experimental results show that the proposed GPFD feature is not only superior to the previously-used SMR features (mu and beta band powers of EEG from sensorimotor cortex) but also better than HFD. The accuracies achieved by the SMR features are not satisfactory (all lower than 80%) in all binary classification tasks, including RH imagery vs. resting, LH imagery vs. resting, and LF imagery vs. resting. For the discrimination between RH imagery and resting, the average accuracies of GPFD in 30-channel (without channel selection) and top-five-channel configurations are 95.25% and 93.50%, respectively. When using only one channel (the best channel among the 30), a high accuracy of 91.00% can still be achieved by the GPFD feature and a linear discriminant analysis (LDA) classifier. The results also demonstrate that the proposed Fisher’s criterion-based channel selection is capable of removing a large amount of redundant and noisy EEG channels. The proposed GPFD feature extraction combined with the channel selection strategy can be used as the basis for further developing high-accuracy and high-usability motor imagery BCI systems from which the patients with ALS can really benefit.

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Section: Introductionmentioning

confidence: 99%

Motor Imagery EEG Classification for Patients with Amyotrophic Lateral Sclerosis Using Fractal Dimension and Fisher’s Criterion-Based Channel Selection

Liu

Huang

2017

Sensors

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“…We utilized a set of stimulus-free two-task BCI control strategies that were previously evaluated in laboratory experiments with healthy subjects. It was shown that both BCI control strategies could be used in a laboratory study to induce significant differences in neural activity [20,19,27,16]. Now, we complement the laboratory results with MYND to investigate whether these strategies can be executed in an uncontrolled, realistic environment, by utilizing a consumer-grade EEG, and if repeated execution affects performance.…”

Section: Choice Of Laboratory-based Bci Control Strategiesmentioning

confidence: 92%

“…MYND was developed to evaluate BCI control strategies in a realistic scenario on consumer-grade hardware. In the current study, we present results for the two strategies "Positive memories" [20] and "Music imagery" [16]. Single-session laboratory studies showed that both strategies feature two tasks that modulate neural oscillations in broad areas of the parietal and prefrontal cortex.…”

Section: The Current Studymentioning

confidence: 99%

“…In the "Positive memories" strategy, subjects are asked to switch between thinking about a positive memory of their past or consecutively subtract a small number from a larger number [20,19,27]. Changing between daydreaming and a task that requires attention modulates activity in the "default mode network," a large-scale network that is involved in selfreferential processes [13,39].…”

Section: Choice Of Laboratory-based Bci Control Strategiesmentioning

confidence: 99%

“…Further, it was found that the dominant frequency of the EEG spectrum, the "alpha peak frequency," is modulated by these strategies as well [27]. In figure 3, we used the laboratory data from previous studies [20,16] to illustrate the induced band-power modulations parietal and prefrontal cortex with the two BCI control strategies. We computed the coefficient of determination (R 2 ) per subject and channel to visualize the average induced differences in normalized thetaalpha-and beta-bands, as well as the dominant frequency of the EEG spectrum.…”

Section: Choice Of Laboratory-based Bci Control Strategiesmentioning

confidence: 99%

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Mynd

Hohmann

Konieczny

Hackl

et al. 2020

Proceedings of the 33rd Annual ACM Symposium on User Interface Software and Technology

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Neurophysiological laboratory studies are often constraint to immediate geographical surroundings and access to equipment may be temporally restricted. Limitations of ecological validity, scalability, and generalizability of findings pose a significant challenge for the development of brain-computer interfaces (BCIs), which ultimately need to function in any context, on consumer-grade hardware. We introduce MYND: An open-source framework that couples consumer-grade recording hardware with an easy-to-use application for the unsupervised evaluation of BCI control strategies. Subjects are guided through experiment selection, hardware fitting, recording, and data upload in order to self-administer multi-day studies that include neurophysiological recordings and questionnaires at home. As a use case, thirty subjects evaluated two BCI control strategies ("Positive memories" and "Music imagery") by using a four-channel electroencephalogram (EEG) with MYND. Neural activity in both control strategies could be decoded with an average offline accuracy of 68.5% and 64.0% across all days.

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An Artificial Neural Network approach for electroencephalographic signal classification towards brain-computer interface implementation

Hoang

Dung

Thang

et al. 2016

2016 IEEE RIVF International Conference on Computing &Amp; Communication Technologies, Research, Innovation, and Vision for The

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A Cognitive Brain-Computer Interface for Patients with Amyotrophic Lateral Sclerosis

Cited by 3 publications

References 26 publications

Motor Imagery EEG Classification for Patients with Amyotrophic Lateral Sclerosis Using Fractal Dimension and Fisher’s Criterion-Based Channel Selection

Motor Imagery EEG Classification for Patients with Amyotrophic Lateral Sclerosis Using Fractal Dimension and Fisher’s Criterion-Based Channel Selection

Mynd

An Artificial Neural Network approach for electroencephalographic signal classification towards brain-computer interface implementation

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