Brain-Computer Interface (BCI), in essence, aims at controlling different assistive devices through the utilization of brain waves. It is worth noting that the application of BCI is not limited to medical applications, and hence, the research in this field has gained due attention. Moreover, the significant number of related publications over the past two decades further indicates the consistent improvements and breakthroughs that have been made in this particular field. Nonetheless, it is also worth mentioning that with these improvements, new challenges are constantly discovered. This article provides a comprehensive review of the state-of-the-art of a complete BCI system. First, a brief overview of electroencephalogram (EEG)-based BCI systems is given. Secondly, a considerable number of popular BCI applications are reviewed in terms of electrophysiological control signals, feature extraction, classification algorithms, and performance evaluation metrics. Finally, the challenges to the recent BCI systems are discussed, and possible solutions to mitigate the issues are recommended.

/pdf/current-status-challenges-and-possible-solutions-of-eeg-4el4wek5td.pdf

Current Status, Challenges, and Possible Solutions of EEG-Based Brain-Computer Interface: A Comprehensive Review

This paper will focus on electroencephalogram (EEG) signal analysis with an emphasis on common feature extraction techniques mentioned in the research literature, as well as a variety of applications that this can be applied to. In this review, we cover single and multi-dimensional EEG signal processing and feature extraction techniques in the time domain, frequency domain, decomposition domain, time-frequency domain, and spatial domain. We also provide pseudocode for the methods discussed so that they can be replicated by practitioners and researchers in their specific areas of biomedical work. Furthermore, we discuss artificial intelligence applications such as assistive technology, neurological disease classification, brain-computer interface systems, as well as their machine learning integration counterparts, to complete the overall pipeline design for EEG signal analysis. Finally, we discuss future work that can be innovated in the feature extraction domain for EEG signal analysis.

/pdf/trends-in-eeg-signal-feature-extraction-applications-1lzz9srb.pdf

Trends in EEG signal feature extraction applications

The evoked potential is a neuronal activity that originates when a stimulus is presented. To achieve its detection, various techniques of brain signal processing can be used. One of the most studied evoked potentials is the P300 brain wave, which usually appears between 300 and 500 ms after the stimulus. Currently, the detection of P300 evoked potentials is of great importance due to its unique properties that allow the development of applications such as spellers, lie detectors, and diagnosis of psychiatric disorders. The present study was developed to demonstrate the usefulness of the Stockwell transform in the process of identifying P300 evoked potentials using a low-cost electroencephalography (EEG) device with only two brain sensors. The acquisition of signals was carried out using the Emotiv EPOC® device—a wireless EEG headset. In the feature extraction, the Stockwell transform was used to obtain time-frequency information. The algorithms of linear discriminant analysis and a support vector machine were used in the classification process. The experiments were carried out with 10 participants; men with an average age of 25.3 years in good health. In general, a good performance (75–92%) was obtained in identifying P300 evoked potentials.

Use of the Stockwell Transform in the Detection of P300 Evoked Potentials with Low-Cost Brain Sensors.

An efficient fusion algorithm based on hybrid multiscale decomposition for infrared-visible and multi-type images

This article proposes a classification method of two-class motor imagery electroencephalogram (EEG) signals based on convolutional neural network (CNN), in which EEG signals from C3, C4 and Cz electrodes of publicly available BCI competition IV dataset 2b were used to test the performance of the CNN. The authors investigate two similar CNNs: a single-input CNN with a form of 2-dimensional input from short time Fourier transform (STFT) combining time, frequency and location information, and a multiple-input CNN with 3-dimensional input which processes the electrodes as an independent dimension. Fisher discriminant analysis-type F-score based on band pass (BP) feature and power spectra density (PSD) feature are employed respectively to select the subject-optimal frequency bands. In the experiments, typical frequency bands related to motor imagery EEG signals, subject-optimal frequency bands and extension frequency bands are employed respectively as the frequency range of the input image of CNN. The better classification performance of extension frequency bands show that CNN can extract optimal feature from frequency information automatically. The classification result also demonstrates that the proposed approach is more competitive in prediction of left/right hand motor imagery task compared with other state-of-art approaches.

Simple Convolutional Neural Network for Left-Right Hands Motor Imagery EEG Signals Classification

P300 Speller is a P300-based Brain-Computer Interfaces (BCIs), which help users write the desired characters to the computer screen by detecting the P300 event-related potentials in users' electroencephalographic (EEG). This article proposes a feature extraction method for P300 detection. This method combines discrete wavelet transform (DWT) with Fisher criterion. This method, firstly transforms the EEG signal into wavelet domain by DWT. Then, the feature space where best distinguish two kinds of EEG signals will be found by means of Fisher criterion. Finally, a feature extraction matrix will be constructed to extract feature vector from EEG signals. The method was tested on EEG data that obtained by P300 Speller paradigm. The result shows that, the proposed method is better than the existing method used in BCI2000, in terms of averaged accuracy over 238 runs.

Feature extraction of P300s in EEG signal with discrete wavelet transform and fisher criterion

In this paper, autoregressive (AR) model coefficients and support vector machine (SVM) are used to classify the motor imagery EEG available from the well-known BCI competition database. In order to determine AR order, we use paired t-test to assess the impact of AR order on the classification precision of motor imagery EEG. The results show that there is a significant difference in the classification performance when the different AR orders are used to model motor imagery EEG. In this investigation, 12-order prevails. We try using the method of continuous re-training the SVM classifier to improve the classification precision of motor imagery EEG, and the experimental results show that the method is feasible and effective.

Determining AR order for BCI based on motor imagery

The invention relates to an AR coefficient space based ELM (extreme learning machine) motor imagination electroencephalogram classification method. The method includes: taking a single-channel motor imagination electroencephalogram signal as a random signal, and adopting an order-p AR model for fitting; solving to-be-determined coefficients a1, a2,... , ap of the AR model according to a Burg algorithm; combining order-p AR coefficients of m-channel motor imagination electroencephalogram signal into an AR coefficient vector, inserting into an original optimization problem of ELM network parameter training to solve an optimal outer weight beta, and establishing an AR coefficient space based ELM classification algorithm. By the AR coefficient space based ELM motor imagination electroencephalogram classification method, accuracy in classification of the motor imagination electroencephalogram signals is improved while classification speed of the motor imagination electroencephalogram signals is increased.

AR coefficient space based ELM (extreme learning machine) motor imagination electroencephalogram classification method

The present invention discloses a brain-computer interface control method for simulating a keyboard and a mouse. The method allows a user to transmit a control command to a controlled device by means of only cognitive activities in the brain in a manner similar to comprehensive use of a keyboard and a mouse. The manner is highly versatile. A brain-computer interface system that implements the method has three states: a resting state, a mouse state, and a keyboard state. A user expresses an intention by staring at blinking in different areas on a screen of the system or by performing motor imagery; the brain-computer interface system detects whether SSVEP, P300, or an event related desynchronization phenomenon corresponding to the motor imagery exists in the electroencephalogram of the user, so as to determine switching of the brain-computer interface system between the three states; the brain-computer interface system in the resting state does not receive user input, the brain-computer interface system in the mouse state allows the user to transmit a control command to a controlled device by using a virtual mouse, and the brain-computer interface system in the keyboard state allows the user to transmit a control command to a controlled device by using a virtual keyboard.

Brain-computer interface control method for simulating keyboard and mouse

Based on combination between wavelet transform and a Fisher criterion, the invention provides an EEG feature extraction method specific to a P300 Speller Brain-machine interface, mainly comprising the steps of: configuring a wavelet transform matrix for an EEG data segment of a specific length according to a wavelet function and a wavelet decomposition level designated by a user; utilizing a wavelet transform matrix to map the EEG data segment to a wavelet domain, and utilizing the Fisher criterion to search for a projection axis which can distinguish different types of data to a maximum degree in the wavelet domain; utilizing the projection axis to extract a plurality of rows from the wavelet transform matrix to form a feature extraction matrix; mapping a corresponding EEG data segment to a feature vector by a feature extraction matrix of each channel; and splicing feature vectors on all channels to form a feature vector for each time of stimulation. The method stimulates a corresponding EEG data segment each time to calculate a feature vector, and can reduce times of stimulation repetition under the condition of meeting accuracy rate requirements so as to increases a character transfer rate.

P300 feature extraction method based on wavelet transformation and Fisher criterion

Handle everyday research tasks with reliable, citation-backed results

Your personal Research Agent to handle research tasks with citation-backed results

Popular Tasks used by Researchers

How can I help with your research?

Meet SciSpace

Get more enhanced response by uploading the PDFs you want me to reference.

No relevant PDFs in your library

SciSpace is the AI research assistant for academics. Run systematic literature reviews on 280M+ papers, and write papers with cited sources. Trusted by 1M+ students, PhDs & researchers.

SciSpace | AI for Research

Analyze PDFs

Code & Manuscripts

Funding & Grants

Literature & Patents

Medical & Clinical Data

Systematic Review

Visualize & Present

Web & Data

Build a Google Scholar-like website for your research.

Build a website

Create charts and images for your research

Create a Chart

Write a paper for submission to a journal

Draft a manuscript

Patent Search

Design eye-catching scientific posters in minutes.

Scientific Poster Generation

Systematic Literature Review

One task is running at the moment. Your messages will be shown right after.

Drag and drop or click here to browse

Loved by <highlight>1 million+</highlight> researchers

Extract a list of specific topics and their sources from unstructured text

Topics

Compare and analyze relevant papers that matches with your search

Papers

Get insights from PDFs and bookmarked papers from your library

My library

Recent searches

Try searching for:

Catch AI-generated content in scholarly and non-scholarly content

{ai} Detector

Ai Writer

Get PDF Summaries, highlighted text explanations 

Chat with PDF

Effortlessly create in-text citations and bibliographies in APA and 2,500 other formats

Citation generator

Get explanations, summaries, and answers on academic papers

Ease up your research workflow with {scispace}'s cohort of exciting AI tools

Elevate your academic writing skills and convey your ideas the way you want

Paraphraser

Explore our range of reading and writing tools

Your file is being prepared and should be ready in a few minutes. If it's a large file, it might take a bit longer. You can close this window, and we'll email you the file when it's done.

You have reached a maximum limit of <strong>{limit}</strong> columns in the table. Remove at least <strong>1</strong> column to add or create another one.

Lin Suyun

Author Tools

Chat about Author