Limitations, possibilities and implications of Brain-Computer Interfaces

Dietrich, Dietmar; Lang, Roland; Bruckner, Dietmar; Fodor, Georg; Muller, B.

doi:10.1109/hsi.2010.5514488

Cited by 14 publications

(5 citation statements)

References 4 publications

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“…BCI technology is a potent instrument for user-system communication, according to other writers, who note that it does not need any outside gadgets or human intervention to issue orders and complete interactions [42]. Current researchers believe that BCI provides a direct line of communication between the brain and an outside equipment [43][44][45][46][47]. BCIs quantify the activity of central nervous system (CNS) and transform it into artificial production that may subsequently be utilized to replace, improve, repair or complement genuine CNS output [41].…”

Section: Bci Contextualization 21 Conceptmentioning

confidence: 99%

An Overview of Mindwave Applications: Study Cases

Teixeira,

Gomes,

Brito-Costa

2023

Artificial Intelligence

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Brain-computer interfaces (BCIs) have diverse applications across various research domains. In healthcare, individuals with disabilities in communication and controlling prosthetic devices are aided. Beyond healthcare, BCIs integrate seamlessly into Internet of Things (IoT) and smart environments, enabling intuitive device control and interaction, enhancing user experiences. In neuromarketing and advertising, BCIs help decipher consumers’ preferences and emotional responses to products and services, providing businesses with profound insights into consumer behavior. In education and self-regulation, BCIs monitor and regulate students’ cognitive states. BCIs use sensors and hardware to capture brain signals, with non-invasive electroencephalography (EEG) technology being a pivotal component. Preliminary studies analyzing cognitive load using EEG signals and the Mindwave device pave the way for measuring student learning outcomes, shedding light on cognitive and neurological learning processes. Our research explores these parameters, particularly the Mindwave system, aiming to understand brain function across domains. To this end, we conduct a range of diversified studies, trying to better grasp parameters such as attention, concentration, stress, immersion, and fatigue during various tasks. Ultimately, our work seeks to harness BCIs’ potential to improve our understanding of brain function and enhance various areas of knowledge.

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Section: Bci Contextualization 21 Conceptmentioning

confidence: 99%

An Overview of Mindwave Applications: Study Cases

Teixeira,

Gomes,

Brito-Costa

2023

Artificial Intelligence

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“…This change in paradigm from "let the user learn" to "let the machine learn" largely reduced training times and significantly increased the attraction of these systems (Blankertz et al, 2006a). Although technology has rapidly advanced during the last decade, e.g., dry electrode EEG recordings (Popescu et al, 2007), zero training systems Fazli et al, 2009) and robust machine learning methods (Lotte and Guan, 2011;, many challenges limiting a large scale application of BCIs in clinical practice and its usage as assistive technology for disabled people still exists (Dietrich et al, 2010;Krusienski et al, 2011;Lance et al, 2012). Several pilot studies Lim et al, 2012) have demonstrated the utility of BCI for medical application, but much more research is needed in this direction.…”

Section: Part I R E V I S I T I N G B C Imentioning

confidence: 99%

On robust spatial filtering of EEG in nonstationary environments

Samek

2016

It - Information Technology

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bibliography 119 I N T R O D U C T I O NVariability is the law of life, and as no two faces are the same, so no two bodies are alike, and no two individuals react alike and behave alike under the abnormal conditions which we know as disease. (Sir William Osler, 1903) T he Canadian physician Sir William Osler (1849 -1919 revolutionized the medical world of the 19th century by advocating an approach to therapy which focuses on the needs of individual patients. He recognized the great variability among individuals with respect to their physical conditions, their mental status and their responses to drugs and concluded that focusing solely on clinical signs and symptoms does not result in the most effective therapy. The development of this novel patient-centered paradigm made him the father of personalized medicine; a branch of medicine that is also very popular today due to a better understanding of the genetic influences on a person's risk of acquiring certain diseases.In the last two decades personalized approaches have also revolutionized the field of Brain-Computer Interfacing (BCI). A BCI system (see e.g. Wolpaw et al., 2002;Dornhege et al., 2007;Wolpaw and Wolpaw, 2012) aims to decode the intention of a user from recordings of brain activity and to use this information for controlling a computer application or a robotic device. Today's BCIs extract user-specific features from electroencelographic (EEG) recordings and adapt to the user's signal characteristics. This subject-centered or machine learning approach not only largely reduces the calibration time in comparison to classical BCI systems based on neurofeedback training (Kamiya et al., 1969) but also increases classification accuracy. Unfortunately, EEG responses to a stimulus or a task not only differ from subject to subject but also from trial to trial and from day to day. This change in the signal properties over time is termed the intrinsic nonstationarity of EEG (Kaplan et al., 2005) and constitutes a major challenge for data analysis and classification by violating a basic assumption of many machine learning methods, namely that data are sampled from a fixed (but unknown) distribution (Vapnik, 1998;Hastie et al., 2001). The analysis of EEG is further aggravated by the presence of artifacts in the data, e.g., eye movements, muscular activity or loose electrodes. The lack of robustness to unexpected events and the nonstationary nature of EEG are major reasons in explaining why current BCI technology is seldom used in out-of-lab scenarios and clinical practice. Since artifacts and nonstationarity can not be fully eliminated, even with the best experimental protocol, robust and invariant feature 1 2 introduction representations are required for optimal signal analysis and high classification accuracy. structure of the thesisThis thesis is divided into three parts. The first part introduces the basic concepts of motor imagery-based Brain-Computer Interfacing and discusses the advantages and limitations of several state-of-the-art spatial filtering al...

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“…EEG-based BCIs are divided into two classes based on the operation mode: dependent (cue-paced or synchronous) and independent (self-paced or asynchronous) [9]. In any case, the functionalities of EEG-based BCIs can be divided into four subsystems: signal acquisition, signal processing, translation of signal features into commands, and the application of the BCI for a specific purpose [10]. The schematic diagram of BCI is described as in Fig.…”

Section: Introductionmentioning

confidence: 99%

Enhancing EEG Signals in Brain Computer Interface Using Wavelet Transform

Mohamed¹

2014

IJIEE

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Brain-computer interface (BCI) is a hardware and software communication system that enables humans to interact with their surrounding without the involvement of peripheral nerves and muscles by using control signals generated from electroencephalographic activity. In this paper, we report on results of developing motor imagery feature extraction method for BCI. The wavelet coefficients were used to extract the features from the motor imagery EEG and the Bayes Net, SVM and RBFN were utilized to classify the pattern of left, right hand movement and forward imagery. The performance was tested using dataset from BCI competition III and satisfactory results are obtained with accuracy rate as high as 99.0674%.

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Limitations, possibilities and implications of Brain-Computer Interfaces

Cited by 14 publications

References 4 publications

An Overview of Mindwave Applications: Study Cases

An Overview of Mindwave Applications: Study Cases

On robust spatial filtering of EEG in nonstationary environments

Enhancing EEG Signals in Brain Computer Interface Using Wavelet Transform

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