Low Information Transfer Rate Research Articles

Performance improvement of P300-based home appliances control classification using convolution neural network

P300 speller-based brain-computer interface (BCI) is an immediate correspondence from a human brain to a computer that depends on the translation of mind reactions produced by stimulus of a subject utilizing a P300 speller. No muscle movements are required for this communication. The present study helps the disabled people (viz.patients with spinal cord injury, spastic cerebral palsy, locomotive diseases, etc.) ease their lives by accessing light, fan, mobile device, door, television, electric heater, etc. A novel 2 × 3 matrix consisting of home appliances visuals has been proposed as a P300 paradigm. Once the matrix visualization is completed, auditory feedback was provided and the chosen command was executed. It enables six menus to be navigated for hadling six electronic systems with up to 30 control commands. The objective of this research is to develop a P300-based BCI system for operating different electronic appliances at home. The existing BCI-based P300 spellers with standard machine learning methods suffers from low information transfer rate (ITR) and poor classification accuracy. With the application of convolution neural network (CNN) for classification, the proposed system improves the performance in terms of P300 classification & target appliances detection. The proposed system is designed to be user-convenient & cost-effective in terms of hardware design. The experiments have been performed on the dataset acquired from 30 target item selections from nine subjects using 16 channel actiCAP Xpress EEG recorders. The experimental result demonstrates that the proposed CNN model achieved 90% classification accuracy. It also offers ITR of 22.3 bits per minute which is substantially greater than the existing methods. The presented approach shows its novelty in the real-time applications.

Brain–computer interface‐based single trial P300 detection for home environment application

P300 speller-based brain–computer interface (BCI) is an immediate correspondence between the human brain and computer that depends on the translation of mind reactions produced by the stimulus of a subject utilising the P300 speller. No muscle movements are required for this communication. As a P300 paradigm, a novel 2 × 3 matrix consisting of visual home appliances is proposed, which helps disabled people ease their lives by accessing mobile, light, fan, door, television, electric heater etc. In most of the current P300-based BCIs, 5–15 trials work better and the low information transfer rate (ITR) is a major issue in its adaptation in real-time. The objective of this Letter is to improve accuracy as well as an ITR for real-time home appliance control applications. To address this, the authors proposed a single trial weighted ensemble of compact convolution neural network and obtained an ITR of 46.45 bits per minute and an average target appliance accuracy of 93.22% for the BCI-based home environment system. The experimental findings confirmed the feasibility of the proposed method and thus can provide guidance for future use of the system for paralysed patients.

A novel hybrid paradigm based on steady state visually evoked potential & P300 to enhance information transfer rate

BackgroundSteady-State Visually Evoked Potential (SSVEP) has been one of the most common paradigms for Brain-Computer Interface (BCI) based applications with relatively good accuracy and Information Transfer Rate (ITR). However, limited decision options for a set number of paradigm frequencies, which is the main reason behind low ITR, is a huge hurdle the researchers have been facing in generalising SSVEP based BCI applications. New methodThis paper proposes a novel hybrid Brain-Computer Interface (hBCI) stimuli paradigm to improve ITR by increasing the number of decision options available for SSVEP BCI by introducing P300 as a Time Division Multiplexing (TDM) marker. One of the Hybrid BCI’s used distinct colours along with distinct flickering frequencies for targets, with an aim to improve the performance of the BCI based on the accuracy of classification, the elevation of ITR and reduction of FAR as compared to traditional BCIs. ResultsIt was established that the Novel SSVEP-P300 with distinct colours for target frequencies hybrid BCI had average parameters as following: classification accuracy of 92.30 %, ITR of 82.38 bits/min and FAR of 2.72 %. ComparisonA comparative study between the two novel paradigms, traditional SSVEP and P300 paradigms in the same environment was conducted. And a statistical inference was established sussing pairedt-tests. ConclusionThe results of the comparative study were conclusive that the hybrid BCI with distinct colours for each target frequency yielded best results and hence can be considered as a viable paradigm option for the development of an Assistive Device.

Optimal signal processing algorithm at the background of non-Gaussian flicker noise

The problem of receiving and processing ultra-low-power signals of information transmission systems is being solved. High requirements for energy efficiency on the one hand and a low information transfer rate allows the use of signals with a small spectrum width, including flicker noise spectral regions. A non-Gaussian flicker noise model is used based on a stochastic differential equation with a nonlinear drift coefficient. An optimal signal processing algorithm is being developed against the background of the sum of flicker noise and thermal noise based on an estimated-correlation-compensation approach. The analysis of the effectiveness of optimal signal processing against a background of non-Gaussian flicker noise and thermal noise is carried out.

Improving Performance of Devanagari Script Input-Based P300 Speller Using Deep Learning.

The performance of an existing Devanagari script (DS) input-based P300 speller with conventional machine learning techniques suffers from low information transfer rate (ITR). This occurs due to its required large size of display, i.e., 8 × 8 row-column (RC) paradigm which exhibits issues like crowding effect, adjacency, fatigue, task difficulty, and required large number of trials for character recognition. For P300 detection, deep learning algorithms have shown the state of art performance compared to the conventional machine learning algorithms in the recent past. Therefore, authors have been motivated to develop a deep learning architecture for DS-based P300 speller which can detect the target characters more accurately and in less number of trials. For this, two proven deep learning algorithms, stacked autoencoder (SAE) and deep convolution neural network (DCNN) have been adopted. For further bettering their performances, batch normalization and innovative double batch training is included here to achieve accelerated training and alleviate the problem of overfitting. Additionally, a leaky ReLU activation function has also been used in DCNN to overcome dying ReLU problem. The experiments have been performed on self-generated dataset of 20 Devanagari words with 79 characters acquired from 10 subjects using 16 channel actiCAP Xpress EEG recorder. The experimental results illustrated that the proposed DCNN is able to detect 88.22% correct targets in just three trials. Moreover, it also provides ITR of 20.58 bits per minutes, which is significantly higher than existing techniques.

VEP-based brain-computer interfaces modulated by Golay complementary series for improving performance.

The goal of a brain-computer interface (BCI) is to enable communication by pure brain activity without neural and muscle control. However, the practical use of BCIs is limited by low information transfer rate. Recently, code modulation visual evoked potential (c-VEP) based BCIs have exhibited great potential in establishing high-rate communication between the brain and the external world. This study aims at exploring a more effective modulation code than the commonly used pseudorandom M sequence for c-VEP based BCIs (c-VEP BCIs) in order to increase the detection accuracy of stimulus targets and the resulting information transfer rate. Golay complementary sequence pair is used for constructing the modulation code of c-VEP BCIs due to their superior autocorrelation property. The modulation code is created by concatenating a pair of Golay complementary sequences. Sixteen target stimuli are modulated by the Golay code and its time shift versions. Through offline analysis on data recorded from seven subjects and online test on five subjects, the Golay code modulated BCI yielded higher detection accuracy and information transfer rate than those achieved by M sequence. The Golay code modulated BCI demonstrates a high performance compared with the M sequence modulated systems, and it is applicable to persons with motor disabilities.

Using a cVEP-Based Brain-Computer Interface to Control a Virtual Agent.

Brain-computer interfaces provide a means for controlling a device by brain activity alone. One major drawback of noninvasive BCIs is their low information transfer rate, obstructing a wider deployment outside the lab. BCIs based on codebook visually evoked potentials (cVEP) outperform all other state-of-the-art systems in that regard. Previous work investigated cVEPs for spelling applications. We present the first cVEP-based BCI for use in real-world settings to accomplish everyday tasks such as navigation or action selection. To this end, we developed and evaluated a cVEP-based on-line BCI that controls a virtual agent in a simulated, but realistic, 3-D kitchen scenario. We show that cVEPs can be reliably triggered with stimuli in less restricted presentation schemes, such as on dynamic, changing backgrounds. We introduce a novel, dynamic repetition algorithm that allows for optimizing the balance between accuracy and speed individually for each user. Using these novel mechanisms in a 12-command cVEP-BCI in the 3-D simulation results in ITRs of 50 bits/min on average and 68 bits/min maximum. Thus, this work supports the notion of cVEP-BCIs as a particular fast and robust approach suitable for real-world use.

Use of a Green Familiar Faces Paradigm Improves P300-Speller Brain-Computer Interface Performance.

BackgroundA recent study showed improved performance of the P300-speller when the flashing row or column was overlaid with translucent pictures of familiar faces (FF spelling paradigm). However, the performance of the P300-speller is not yet satisfactory due to its low classification accuracy and information transfer rate.ObjectiveTo investigate whether P300-speller performance is further improved when the chromatic property and the FF spelling paradigm are combined.MethodsWe proposed a new spelling paradigm in which the flashing row or column is overlaid with translucent green pictures of familiar faces (GFF spelling paradigm). We analyzed the ERP waveforms elicited by the FF and proposed GFF spelling paradigms and compared P300-speller performance between the two paradigms.ResultsSignificant differences in the amplitudes of four ERP components (N170, VPP, P300, and P600f) were observed between both spelling paradigms. Compared to the FF spelling paradigm, the GFF spelling paradigm elicited ERP waveforms of higher amplitudes and resulted in improved P300-speller performance.ConclusionsCombining the chromatic property (green color) and the FF spelling paradigm led to better classification accuracy and an increased information transfer rate. These findings demonstrate a promising new approach for improving the performance of the P300-speller.

SBCI-Headset—Wearable and Modular Device for Hybrid Brain-Computer Interface

Severely disabled people, like completely paralyzed persons either with tetraplegia or similar disabilities who cannot use their arms and hands, are often considered as a user group of Brain Computer Interfaces (BCI). In order to achieve high acceptance of the BCI by this user group and their supporters, the BCI system has to be integrated into their support infrastructure. Critical disadvantages of a BCI are the time consuming preparation of the user for the electroencephalography (EEG) measurements and the low information transfer rate of EEG based BCI. These disadvantages become apparent if a BCI is used to control complex devices. In this paper, a hybrid BCI is described that enables research for a Human Machine Interface (HMI) that is optimally adapted to requirements of the user and the tasks to be carried out. The solution is based on the integration of a Steady-state visual evoked potential (SSVEP)-BCI, an Event-related (de)-synchronization (ERD/ERS)-BCI, an eye tracker, an environmental observation camera, and a new EEG head cap for wearing comfort and easy preparation. The design of the new fast multimodal BCI (called sBCI) system is described and first test results, obtained in experiments with six healthy subjects, are presented. The sBCI concept may also become useful for healthy people in cases where a “hands-free” handling of devices is necessary.

A high-ITR SSVEP-based BCI speller

Spelling is an important application of brain-computer interfaces (BCIs). Previous BCI spellers were not suited for widespread use due to their low information transfer rate (ITR). In this study, we constructed a high-ITR BCI speller based on the steady-state visual evoked potential (SSVEP). A 45-target BCI speller was implemented with a frequency resolution of 0.2 Hz. A sampled sinusoidal stimulation method was used to present visual stimuli on a conventional LCD screen. The online results revealed that the proposed BCI speller had a good performance, reaching a high average accuracy (84.1% for 2 s stimulation time; 90.2% for 3 s stimulation time) and the corresponding high ITR (105 bits/min for 2 s stimulation time, 82 bits/min for 3 s stimulation time) during the low-frequency stimuli, while 88.7% and 61 bits/min were achieved for a 4 s time window during the high-frequency stimuli.

A Review of Hybrid Brain-Computer Interface Systems

Increasing number of research activities and different types of studies in brain-computer interface (BCI) systems show potential in this young research area. Research teams have studied features of different data acquisition techniques, brain activity patterns, feature extraction techniques, methods of classifications, and many other aspects of a BCI system. However, conventional BCIs have not become totally applicable, due to the lack of high accuracy, reliability, low information transfer rate, and user acceptability. A new approach to create a more reliable BCI that takes advantage of each system is to combine two or more BCI systems with different brain activity patterns or different input signal sources. This type of BCI, called hybrid BCI, may reduce disadvantages of each conventional BCI system. In addition, hybrid BCIs may create more applications and possibly increase the accuracy and the information transfer rate. However, the type of BCIs and their combinations should be considered carefully. In this paper, after introducing several types of BCIs and their combinations, we review and discuss hybrid BCIs, different possibilities to combine them, and their advantages and disadvantages.

A Brain Controlled Wheelchair to Navigate in Familiar Environments

While brain-computer interfaces (BCIs) can provide communication to people who are locked-in, they suffer from a very low information transfer rate. Further, using a BCI requires a concentration effort and using it continuously can be tiring. The brain controlled wheelchair (BCW) described in this paper aims at providing mobility to BCI users despite these limitations, in a safe and efficient way. Using a slow but reliable P300 based BCI, the user selects a destination amongst a list of predefined locations. While the wheelchair moves on virtual guiding paths ensuring smooth, safe, and predictable trajectories, the user can stop the wheelchair by using a faster BCI. Experiments with nondisabled subjects demonstrated the efficiency of this strategy. Brain control was not affected when the wheelchair was in motion, and the BCW enabled the users to move to various locations in less time and with significantly less control effort than other control strategies proposed in the literature.