Wheelchair simulator game for training people with severe disabilities

Pinheiro, Oberdan Rocha; Alves, Lynn; Romero, M. F. M.; Souza, Josemar Rodrigues de

doi:10.1109/tishw.2016.7847792

Cited by 32 publications

(24 citation statements)

References 14 publications

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“…Assume a batch of input EEG data contains n bs (generally called batch size) EEG samples and the total input data has the 3-D shape as [n bs , 1, 64]. Let the data in the ith layer (i = 1, 2, · · · , 7) be denoted by 1,Ki] , where j denotes the j-th EEG sample and K i denotes the number of dimensions in the i-th layer.…”

Section: B Deep Feature Learningmentioning

confidence: 99%

“…Please note the sizes of X r i , W r i,i+1 and b r i must match. For example, in Figure 1, the transformation between H1 layer and H2 layer, the sizes of X r 3 , X r 2 , W [2,3] , and b r 2 are separately [1, 1, 64], [1,1,64], [64,64], and [1,64].…”

Section: B Deep Feature Learningmentioning

confidence: 99%

“…Brain-computer interface (BCI) systems have been widely studied for various real-world applications from mindcontrolled service robots in the healthcare domain [1] to enriched video gaming in the entertainment industry [2]. As an important pathway between human brains and the outside world [3], BCI systems allow people to communicate or interact with external devices such as wheelchairs or service robots, through their brain signals.…”

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Converting Your Thoughts to Texts: Enabling Brain Typing via Deep Feature Learning of EEG Signals

Zhang

Yao

Sheng

et al. 2018

2018 IEEE International Conference on Pervasive Computing and Communications (PerCom)

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An electroencephalography (EEG) based Brain Computer Interface (BCI) enables people to communicate with the outside world by interpreting the EEG signals of their brains to interact with devices such as wheelchairs and intelligent robots. More specifically, motor imagery EEG (MI-EEG), which reflects a subject's active intent, is attracting increasing attention for a variety of BCI applications. Accurate classification of MI-EEG signals while essential for effective operation of BCI systems, is challenging due to the significant noise inherent in the signals and the lack of informative correlation between the signals and brain activities. In this paper, we propose a novel deep neural network based learning framework that affords perceptive insights into the relationship between the MI-EEG data and brain activities. We design a joint convolutional recurrent neural network that simultaneously learns robust high-level feature presentations through low-dimensional dense embeddings from raw MI-EEG signals. We also employ an Autoencoder layer to eliminate various artifacts such as background activities. The proposed approach has been evaluated extensively on a largescale public MI-EEG dataset and a limited but easy-to-deploy dataset collected in our lab. The results show that our approach outperforms a series of baselines and the competitive state-of-theart methods, yielding a classification accuracy of 95.53%. The applicability of our proposed approach is further demonstrated with a practical BCI system for typing.

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Section: B Deep Feature Learningmentioning

confidence: 99%

Section: B Deep Feature Learningmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Converting Your Thoughts to Texts: Enabling Brain Typing via Deep Feature Learning of EEG Signals

Zhang

Yao

Sheng

et al. 2018

2018 IEEE International Conference on Pervasive Computing and Communications (PerCom)

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“…Pinheiro et al [28] adopt a C4.5 decision tree as the classifier to distinguish the manually extracted EEG features such as arithmetic mean and maximum value of the Fourier transform. Kim et al [29] use a multivariate empirical mode decomposition to obtain the mu and beta rhythms from the nonlinear EEG signals.…”

Section: Mir Baselinesmentioning

confidence: 99%

Know Your Mind: Adaptive Cognitive Activity Recognition with Reinforced CNN

Zhang

Yao

Wang

et al. 2019

2019 IEEE International Conference on Data Mining (ICDM)

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Electroencephalography (EEG) signals reflect and measure activities in certain brain areas. Its zero clinical risk and easy-to-use features make it a good choice of providing insights into the cognitive process. However, effective analysis of time-varying EEG signals remains challenging. First, EEG signal processing and feature engineering are time-consuming and highly rely on expert knowledge, and most existing studies focus on domain-specific classification algorithms, which may not apply to other domains. Second, EEG signals usually have low signal-to-noise ratios and are more chaotic than other sensor signals. In this regard, we propose a generic EEG-based cognitive activity recognition framework that can adaptively support a wide range of cognitive applications to address the above issues. The framework uses a reinforced selective attention model to choose the characteristic information among raw EEG signals automatically. It employs a convolutional mapping operation to dynamically transform the selected information into a feature space to uncover the implicit spatial dependency of EEG sample distribution. We demonstrate the effectiveness of the framework under three representative scenarios: intention recognition with motor imagery EEG, person identification, and neurological diagnosis, and further evaluate it on three widely used public datasets. The experimental results show our framework outperforms multiple state-of-the-art baselines and achieves competitive accuracy on all the datasets while achieving low latency and high resilience in handling complex EEG signals across various domains. The results confirm the suitability of the proposed generic approach for a range of problems in the realm of Brain-Computer Interface applications.

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“…The development of brain-computer interface (BCI) systems have valuable applications in areas such as medicine [1], [2], robotics [3], [4], and human entertainment industry [5], [6]. Helping people with movement disabilities to retract their motor functionalities through mental commands and communicate with the digital world is one of the most important consequences of such technology.…”

Section: Introductionmentioning

confidence: 99%

Brain-Computer Interface in Virtual Reality

Abbasi-Asl

Keshavarzi

Chan

2019

2019 9th International IEEE/EMBS Conference on Neural Engineering (NER)

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We study the performance of brain computer interface (BCI) system in a virtual reality (VR) environment and compare it to 2D regular displays. First, we design a headset that consists of three components: a wearable electroencephalography (EEG) device, a VR headset and an interface. Recordings of brain and behavior from human subjects, performing a wide variety of tasks using our device are collected. The tasks consist of object rotation or scaling in VR using either mental commands or facial expression (smile and eyebrow movement). Subjects are asked to repeat similar tasks on regular 2D monitor screens. The performance in 3-D virtual reality environment is considerably higher compared to the to the 2D screen. Particularly, the median number of success rate across trials for VR setting is double of that for the 2D setting (8 successful command in VR setting compared to 4 successful command in 2D screen in 1 minute trials). Our results suggest that the design of future BCI systems can remarkably benefit from the VR setting.

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Wheelchair simulator game for training people with severe disabilities

Cited by 32 publications

References 14 publications

Converting Your Thoughts to Texts: Enabling Brain Typing via Deep Feature Learning of EEG Signals

Converting Your Thoughts to Texts: Enabling Brain Typing via Deep Feature Learning of EEG Signals

Know Your Mind: Adaptive Cognitive Activity Recognition with Reinforced CNN

Brain-Computer Interface in Virtual Reality

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