In recent decades, the automatic recognition and interpretation of brain waves acquired by electroencephalographic (EEG) technologies have undergone remarkable growth, leading to a consequent rapid ...development of brain-computer interfaces (BCIs). EEG-based BCIs are non-invasive systems that allow communication between a human being and an external device interpreting brain activity directly. Thanks to the advances in neurotechnologies, and especially in the field of wearable devices, BCIs are now also employed outside medical and clinical applications. Within this context, this paper proposes a systematic review of EEG-based BCIs, focusing on one of the most promising paradigms based on motor imagery (MI) and limiting the analysis to applications that adopt wearable devices. This review aims to evaluate the maturity levels of these systems, both from the technological and computational points of view. The selection of papers has been performed following the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA), leading to 84 publications considered in the last ten years (from 2012 to 2022). Besides technological and computational aspects, this review also aims to systematically list experimental paradigms and available datasets in order to identify benchmarks and guidelines for the development of new applications and computational models.
The electroencephalographic (EEG) signals provide highly informative data on brain activities and functions. Therefore, it is possible to extract a great variety of features from these data.
The ...heterogeneity and high dimensionality of the EEG signals may represent an obstacle for data interpretation. The introduction of a priori knowledge has been widely employed to mitigate high dimensionality problems, even though it could lose some information and patterns present in the data. Moreover, data heterogeneity remains an open issue that often makes generalization difficult.
In this study, we propose the adoption of a Genetic Algorithm (GA) for feature selection, where we introduced a series of modifications on the stopping criteria and fitness functions only and that can be used with a supervised or unsupervised approach. Our proposal considers three different fitness functions without relying on expert knowledge. Starting from two publicly available datasets on cognitive workload and motor movement/imagery, the EEG signals are processed, normalized and their features computed in the time, frequency and time–frequency domains. The feature vector selection is performed by applying our GA proposal and compared with two benchmarking techniques, i.e., using the entire feature set and reducing it through principal component analysis.
Our proposal experiments achieve better results in respect to the benchmark in terms of overall performance and feature reduction. Moreover, the application of our novel fitness function outperforms the benchmark when the two considered datasets are merged together, showing the effectiveness of our proposal on heterogeneous data. The selected features are compliant with the neuroscientific literature regarding the considered experimental conditions. Future works will focus on providing a better scoring for the unsupervised technique, the hybrid use of the two approaches and the optimization of the GA parameters.
•Genetic algorithm for electroencephalographic data feature selection.•Mitigation of electroencephalographic signal heterogeneity.•Custom learning models, fitness functions and stopping criteria.•Flexible, generalizable and not too computationally demanding proposals.
In this paper we describe and validate a new coordinate-based method for meta-analysis of neuroimaging data based on an optimized hierarchical clustering algorithm: CluB (Clustering the Brain). The ...CluB toolbox permits both to extract a set of spatially coherent clusters of activations from a database of stereotactic coordinates, and to explore each single cluster of activation for its composition according to the cognitive dimensions of interest. This last step, called “cluster composition analysis,” permits to explore neurocognitive effects by adopting a factorial-design logic and by testing the working hypotheses using either asymptotic tests, or exact tests either in a classic inference, or in a Bayesian-like context. To perform our validation study, we selected the fMRI data from 24 normal controls involved in a reading task. We run a standard random-effects second level group analysis to obtain a “Gold Standard” of reference. In a second step, the subject-specific reading effects (i.e., the linear t-contrast “reading > baseline”) were extracted to obtain a coordinates-based database that was used to run a meta-analysis using both CluB and the popular Activation Likelihood Estimation method implemented in the software GingerALE. The results of the two meta-analyses were compared against the “Gold Standard” to compute performance measures, i.e., sensitivity, specificity, and accuracy. The GingerALE method obtained a high level of accuracy (0.967) associated with a high sensitivity (0.728) and specificity (0.971). The CluB method obtained a similar level of accuracy (0.956) and specificity (0.969), notwithstanding a lower level of sensitivity (0.14) due to the lack of prior Gaussian transformation of the data. Finally, the two methods obtained a good-level of concordance (AC
1
= 0.93). These results suggested that methods based on hierarchical clustering (and
post-hoc
statistics) and methods requiring prior Gaussian transformation of the data can be used as complementary tools, with the GingerALE method being optimal for neurofunctional mapping of pooled data according to simpler designs, and the CluB method being preferable to test more specific, and localized, neurocognitive hypotheses according to factorial designs.
•We theoretically describe the expert systems.•We investigate the fuzzy, medical and wearable expert system variations.•We highlight the expert systems advantages and issues.•We emphasize the ...importance of expert system validation.•We describe in depth some expert system applications in the medical field.
The aim of this review is to provide a broad overview of the state-of-the-art works mainly published in the last ten years on expert systems applied in different medical domains.
Being able to support and sometimes substitute experts, an expert system may be a precious ally for medical diagnoses. Medical expert system applications provide physicians and patients with an immediate access to knowledge and advice, rooting their flexibility into their knowledge bases, rule sets and graphical interfaces. To be trusted by their users, medical expert systems should follow some criteria, which we investigate along with their different realization, from fuzzy logic to wearable solutions for out-of-clinical-environment care. We also consider the advantages of approaching diagnoses and alert systems through an artificial intelligence counterpart, without forgetting the importance of a good validation to assess the system functionality.
Therefore, we show the heterogeneity of the solutions proposed by the literature, bounded to the specific needs a medical expert system is called to answer, the common lack of a system validation and the possible benefits deriving from these systems application.
The electroencephalographic (EEG) signals provide highly informative data on brain activities and functions. However, their heterogeneity and high dimensionality may represent an obstacle for their ...interpretation. The introduction of a priori knowledge seems the best option to mitigate high dimensionality problems, but could lose some information and patterns present in the data, while data heterogeneity remains an open issue that often makes generalization difficult. In this study, we propose a genetic algorithm (GA) for feature selection that can be used with a supervised or unsupervised approach. Our proposal considers three different fitness functions without relying on expert knowledge. Starting from two publicly available datasets on cognitive workload and motor movement/imagery, the EEG signals are processed, normalized and their features computed in the time, frequency and time-frequency domains. The feature vector selection is performed by applying our GA proposal and compared with two benchmarking techniques. The results show that different combinations of our proposal achieve better results in respect to the benchmark in terms of overall performance and feature reduction. Moreover, the proposed GA, based on a novel fitness function here presented, outperforms the benchmark when the two different datasets considered are merged together, showing the effectiveness of our proposal on heterogeneous data.
This work focuses on inner speech recognition starting from EEG signals. Inner speech recognition is defined as the internalized process in which the person thinks in pure meanings, generally ...associated with an auditory imagery of own inner "voice". The decoding of the EEG into text should be understood as the classification of a limited number of words (commands) or the presence of phonemes (units of sound that make up words). Speech-related BCIs provide effective vocal communication strategies for controlling devices through speech commands interpreted from brain signals, improving the quality of life of people who have lost the capability to speak, by restoring communication with their environment. Two public inner speech datasets are analysed. Using this data, some classification models are studied and implemented starting from basic methods such as Support Vector Machines, to ensemble methods such as the eXtreme Gradient Boosting classifier up to the use of neural networks such as Long Short Term Memory (LSTM) and Bidirectional Long Short Term Memory (BiLSTM). With the LSTM and BiLSTM models, generally not used in the literature of inner speech recognition, results in line with or superior to those present in the stateof-the-art are obtained.
The ageing process may lead to cognitive and physical impairments, which may affect elderly everyday life. In recent years, the use of Brain Computer Interfaces (BCIs) based on Electroencephalography ...(EEG) has revealed to be particularly effective to promote and enhance rehabilitation procedures, especially by exploiting motor imagery experimental paradigms. Moreover, BCIs seem to increase patients' engagement and have proved to be reliable tools for elderly overall wellness improvement. However, EEG signals usually present a low signal-to-noise ratio and can be recorded for a limited time. Thus, irrelevant information and faulty samples could affect the BCI performance. Introducing a methodology that allows the extraction of informative components from the EEG signal while maintaining its intrinsic characteristics, may provide a solution to both the described issues: noisy data may be avoided by having only relevant components and combining relevant components may represent a good strategy to substitute the data without requiring long or repeated EEG recordings. Moreover, substituting faulty trials may significantly improve the classification performances of a BCI when translating imagined movement to rehabilitation systems. To this end, in this work the EEG signal decomposition by means of multivariate empirical mode decomposition is proposed to obtain its oscillatory modes, called Intrinsic Mode Functions (IMFs). Subsequently, a novel procedure for relevant IMF selection criterion based on the IMF time-frequency representation and entropy is provided. After having verified the reliability of the EEG signal reconstruction with the relevant IMFs only, the relevant IMFs are combined to produce new artificial data and provide new samples to use for BCI training.
The Motor Imagery (MI) electroencephalography (EEG) based Brain Computer Interfaces (BCIs) allow the direct communication between humans and machines by exploiting the neural pathways connected to ...motor imagination. Therefore, these systems open the possibility of developing applications that could span from the medical field to the entertainment industry. In this context, Artificial Intelligence (AI) approaches become of fundamental importance especially when wanting to provide a correct and coherent feedback to BCI users. Moreover, publicly available datasets in the field of MI EEG-based BCIs have been widely exploited to test new techniques from the AI domain. In this work, AI approaches applied to datasets collected in different years and with different devices but with coherent experimental paradigms are investigated with the aim of providing a concise yet sufficiently comprehensive survey on the evolution and influence of AI techniques on MI EEG-based BCI data.
Electroencephalographic (EEG) signals are fundamental to neuroscience research and clinical applications such as brain-computer interfaces and neurological disorder diagnosis. These signals are ...typically a combination of neurological activity and noise, originating from various sources, including physiological artifacts like ocular and muscular movements. Under this setting, we tackle the challenge of distinguishing neurological activity from noise-related sources. We develop a novel EEG denoising model that operates in the frequency domain, leveraging prior knowledge about noise spectral features to adaptively compute optimal convolutional filters for noise separation. The model is trained to learn an empirical relationship connecting the spectral characteristics of noise and noisy signal to a non-linear transformation which allows signal denoising. Performance evaluation on the EEGdenoiseNet dataset shows that the proposed model achieves optimal results according to both temporal and spectral metrics. The model is found to remove physiological artifacts from input EEG data, thus achieving effective EEG denoising. Indeed, the model performance either matches or outperforms that achieved by benchmark models, proving to effectively remove both muscle and ocular artifacts without the need to perform any training on the particular type of artifact.
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