P300-based Brain-computer Interface System Research Articles

Eliminating or Shortening the Calibration for a P300 Brain-Computer Interface Based on a Convolutional Neural Network and Big Electroencephalography Data: An Online Study.

A brain-computer interface (BCI) measures and analyzes brain activity and converts it into computer commands to control external devices. Traditional BCIs usually require full calibration, which is time-consuming and makes BCI systems inconvenient to use. In this study, we propose an online P300 BCI spelling system with zero or shortened calibration based on a convolutional neural network (CNN) and big electroencephalography (EEG) data. Specifically, three methods are proposed to train CNNs for the online detection of P300 potentials: (i) training a subject-independent CNN with data collected from 150 subjects; (ii) adapting the CNN online via a semisupervised learning/self-training method based on unlabeled data collected during the user's online operation; and (iii) fine-tuning the CNN with a transfer learning method based on a small quantity of labeled data collected before the user's online operation. Note that the calibration process is eliminated in the first two methods and dramatically shortened in the third method. Based on these methods, an online P300 spelling system is developed. Twenty subjects participated in our online experiments. Average accuracies of 89.38%, 94.00% and 93.50% were obtained by the subject-independent CNN, the self-training-based CNN and the transfer learning-based CNN, respectively. These results demonstrate the effectiveness of our methods, and thus, the convenience of the online P300-based BCI system is substantially improved.

An optimized deep learning approach based on autoencoder network for P300 detection in brain computer interface systems

Background. Brain computer interface (BCI) systems by extracting knowledge from brain signals provide a connection channel to the outside world for disabled people, without physiological interfaces. Event-related potentials (ERPs) are a specific type of electroencephalography signals and P300 is one of the most important ERP components. The critical part of P300-based BCI systems is classification step. In this research, an approach is proposed for P300 classification based on novel machine learning methods using convolutional neural networks (CNN) and autoencoder networks. Methods. In the pre-processing step, channel selection, data augmentation (by ADASYN method), filtering and base-line drift were done. Then, in the classification step, four different CNN classifiers including CNN1D, CNN2D, CNN1D_Autoencoder, and CNN2D-Autoencoder were used for P300 classification. Results. After implementation and tuning the networks, 92% as a best accuracy was achieved by CNN2D_Autoencoder. This result was achieved with a considerable tradeoff between complexity and stability. Conclusion. The acquired results emphasize the ability of the deep learning methods in P300 classification and approve the advantage of using them in BCI systems. Furthermore, autoencoder versions of CNN networks are more stable and have a faster convergence. Meanwhile, ADASYN is a suitable method for augmentation of P300 data and even ERPs by sustaining the premier feature space without copying data. Practical Implications. Our results can increase the accuracy of P300 detection and simultaneously reduce the volume of data using the proposed model. Consequently, they can improve character recognition in P300-speller systems generally used by amyotrophic lateral sclerosis (ALS) patients.

Feature selection method based on Menger curvature and LDA theory for a P300 brain–computer interface

Objective. Brain–computer interface (BCI) systems decode electroencephalogram (EEG) signals to establish a channel for direct interaction between the human brain and the external world without the need for muscle or nerve control. The P300 speller, one of the most widely used BCI applications, presents a selection of characters to the user and performs character recognition by identifying P300 event-related potentials from the EEG. Such P300-based BCI systems can reach good levels of accuracy but are difficult to use in day-to-day life due to redundancy and noisy signal. A room for improvement should be considered. We propose a novel hybrid feature selection method for the P300-based BCI system to address the problem of feature redundancy, which combines the Menger curvature and linear discriminant analysis. Approach. First, selected strategies are applied separately to a given dataset to estimate the gain for application to each feature. Then, each generated value set is ranked in descending order and judged by a predefined criterion to be suitable in classification models. The intersection of the two approaches is then evaluated to identify an optimal feature subset. Main results. The proposed method is evaluated using three public datasets, i.e. BCI Competition III dataset II, brain/neural computer interaction Horizon dataset, and Lausanne Federal Institute of Technology dataset. Experimental results indicate that compared with other typical feature selection and classification methods, our proposed method has better or comparable performance. Significance. Additionally, our proposed method can achieve the best classification accuracy after all epochs in three datasets. In summary, our proposed method provides a new way to enhance the performance of the P300-based BCI speller.

A robust and reliable online P300-based BCI system using Emotiv EPOC + headset

Brain–computer interface (BCI) system aims to enable interaction with people and therefore the environment without muscular activation, using changes in brain signals due to the execution of cognitive tasks. The target of the presented work is to investigate the power of Emotiv EPOC + headset to detect and record the P300 wave. Moreover, the effect of preprocessing the acquired signal was studied. Five participants were asked to attend different sessions to an equivalent 6x6 matrix while the rows and columns were randomly flashed at a rate of 200 ms. The acquired EEG data were sent wirelessly to OpenViBE software, which is employed to run the P300 speller. Two classification methods were tried: Linear discriminate analysis (LDA) and support vector machine (SVM). The capability of the headset to detect the P300 signals is proven by the results. Additionally, results show that participants reached accuracy up to 90 and 70% after only two training sessions for Linear discriminate analysis (LDA) and support vector machine (SVM) classifiers, respectively. The significance of this work is to demonstrate that such a portable and affordable headset might be useful to design and implement a robust and reliable online P300-based BCI system.

A P300-Based BCI System Using Stereoelectroencephalography and Its Application in a Brain Mechanistic Study.

Stereoelectroencephalography (SEEG) signals can be obtained by implanting deep intracranial electrodes. SEEG depth electrodes can record brain activity from the shallow cortical layer and deep brain structures, which is not achievable through other recording techniques. Moreover, SEEG has the advantage of a high signal-to-noise ratio (SNR). Therefore, it provides a potential way to establish a highly efficient brain-computer interface (BCI) and aid in understanding human brain activity. In this study, we implemented a P300-based BCI using SEEG signals. A single-character oddball paradigm was applied to elicit P300. To predict target characters, we fed the feature vectors extracted from the signals collected by five SEEG contacts into a Bayesian linear discriminant analysis (BLDA) classifier. Thirteen epileptic patients implanted with SEEG electrodes participated in the experiment and achieved an average online spelling accuracy of 93.85%. Moreover, through single-contact decoding analysis and simulated online analysis, we found that the SEEG-based BCI system achieved a high performance even when using a single signal channel. Furthermore, contacts with high decoding accuracies were mainly distributed in the visual ventral pathway, especially the fusiform gyrus (FG) and lingual gyrus (LG), which played an important role in building P300-based SEEG BCIs. These results might provide new insights into P300 mechanistic studies and the corresponding BCIs.

A Novel Method based on Regularized Logistic Regression and CCA for P300 Detection using a Reduced Number of EEG Trials

The Event-Related Potential (ERP) P300 is an Electroencephalography (EEG) signal evoked by external stimuli, characterised by a positive deflection at the 300 ms after presentation of an interesting stimulus for the user. In literature, P300-based Brain-Computer Interface (BCI) has been implemented to translate the subjects intent for restoring communication and control functions. In order to detect the ERP of the EEG signals and taking into account low signal-to-noise ratio (SNR) of the P300 signal, multiple trial averaging has been widely used, which permit diminishing the random noise. However, the signal averaging technique requires processing multiple EEG trials, thus the ITR (Information Transfer Rate) of the P300-based BCI is significantly reduced. An open challenge of P300-based BCI systems focuses on recognizing ERP signals (as target and no-target) using a reduced number of trials with an enough classification rate. In this work, we propose a novel method based on Regularized Logistic Regression (RLR) using as features the coefficients obtained from a Canonical Correlation Analysis (CCA), as method for detection of visual P300 ERPs using a reduced number of EEG trials (1, 2, 3 and 4 trials). The proposed method was evaluated with a freely available EEG dataset and was compared with two widely used methods: Mean-Amplitude LDA and Step-Wise LDA. Finally, the proposed method outperforms standard algorithms for P300 detection, providing a maximum accuracy of 86.3% (p=0.27 to Mean Amplitude LDA and p less than 0.05 to Stepwise-LDA) and maximum Area Under Curve (AUC) of the Receiver Operating characteristic (ROC) of 0.753 (p=0.715 to Mean Amplitude LDA and p=0.228 to Stepwise LDA) on the recognition. Despite the performance of some metrics of proposed method are not significant, the results allow concluding that this one have an acceptable performance for the recognition of P300, using a reduced number of trials.

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.

Importance of Graphical User Interface in the design of P300 based Brain–Computer Interface systems

Objectives: Develop an effective and intuitive Graphical User Interface (GUI) for a Brain–Computer Interface (BCI) system, that achieves high classification accuracy and Information Transfer Rates (ITRs), while using a simple classification technique. Objectives also include the development of an output device, that is capable of real time execution of the selected commands. Methods:A region based T9 BCI system with familiar face presentation cues capable of eliciting strong P300 responses was developed. Electroencephalogram (EEG) signals were collected from the Oz, POz, CPz and Cz electrode locations on the scalp and subsequently filtered, averaged and used to extract two features. These feature sets were classified using the Nearest Neighbour Approach (NNA). To complement the developed BCI system, a ‘drone prototype’ capable of simulating six different movements, each over a range of eight distinct selectable distances, was also developed. This was achieved through the construction of a body with 4 movable legs, capable of tilting the main body forward, backward, up and down, as well as a pointer capable of turning left and right. Results:From ten participants, with normal or corrected to normal vision, an average accuracy of 91.3 ± 4.8% and an ITR of 2.2 ± 1.1 commands/minute (12.2 ± 6.0 bits/minute) was achieved. Conclusion:The proposed system was shown to elicit strong P300 responses. When compared to similar P300 BCI systems, which utilise a variety of more complex classifiers, competitive accuracy and ITR results were achieved, implying the superiority of the proposed GUI. Significance: This study supports the hypothesis that more research, time and care should be taken when developing GUIs for BCI systems.

A TrAdaBoost-based method for detecting multiple subjects' P300 potentials

Individual differences of P300 potentials lead to that a large amount of training data must be collected to construct pattern recognition models in P300-based brain-computer interface system, which may cause subjects' fatigue and degrade the system performance. TrAdaBoost is a method that transfers the knowledge from source area to target area, which improves learning effect in the target area. Our research purposed a TrAdaBoost-based linear discriminant analysis and a TrAdaBoost-based support vector machine to recognize the P300 potentials across multiple subjects. This method first trains two kinds of classifiers separately by using the data deriving from a small amount of data from same subject and a large amount of data from different subjects. Then it combines all the classifiers with different weights. Compared with traditional training methods that use only a small amount of data from same subject or mixed different subjects' data to directly train, our algorithm improved the accuracies by 19.56% and 22.25% respectively, and improved the information transfer rate of 14.69 bits/min and 15.76 bits/min respectively. The results indicate that the TrAdaBoost-based method has the potential to enhance the generalization ability of brain-computer interface on the individual differences.

Towards an accessible use of smartphone-based social networks through brain-computer interfaces

This study presents an asynchronous P300-based Brain–Computer Interface (BCI) system for controlling social networking features of a smartphone. There are very few BCI studies based on these mobile devices and, to the best of our knowledge, none of them supports networking applications or are focused on an assistive context, failing to test their systems with motor-disabled users. Therefore, the aim of the present study is twofold: (i) to design and develop an asynchronous P300-based BCI system that allows users to control Twitter and Telegram in an Android device; and (ii) to test the usefulness of the developed system with a motor-disabled population in order to meet their daily communication needs. Row-col paradigm (RCP) is used in order to elicitate the P300 potentials in the scalp of the user, which are immediately processed for decoding the user’s intentions. The expert system integrates a decision-making stage that analyzes the attention of the user in real-time, providing a comprehensive and asynchronous control. These intentions are then translated into application commands and sent via Bluetooth to the mobile device, which interprets them and provides visual feedback to the user. During the assessment, both qualitative and quantitative metrics were obtained, and a comparison among other state-ofthe-art studies was performed as well. The system was tested with 10 healthy control subjects and 18 motor-disabled subjects, reaching average online accuracies of 92.3% and 80.6%, respectively. Results suggest that the system allows users to successfully control two socializing features of a smartphone, bridging the accessibility gap in these trending devices. Our proposal could become a useful tool within households, rehabilitation centers or even companies, opening up new ways to support the integration of motor-disabled people, and making an impact in their quality of life by improving personal autonomy and self-dependence.

Wavelet multiresolution complex network for decoding brain fatigued behavior from P300 signals

Brain–computer interface (BCI) enables users to interact with the environment without relying on neural pathways and muscles. P300 based BCI systems have been extensively used to achieve human–machine interaction. However, the appearance of fatigue symptoms during operation process leads to the decline in classification accuracy of P300. Characterizing brain cognitive process underlying normal and fatigue conditions constitutes a problem of vital importance in the field of brain science. We in this paper propose a novel wavelet decomposition based complex network method to efficiently analyze the P300 signals recorded in the image stimulus test based on classical ‘Oddball’ paradigm. Initially, multichannel EEG signals are decomposed into wavelet coefficient series. Then we construct complex network by treating electrodes as nodes and determining the connections according to the 2-norm distances between wavelet coefficient series. The analysis of topological structure and statistical index indicates that the properties of brain network demonstrate significant distinctions between normal status and fatigue status. More specifically, the brain network reconfiguration in response to the cognitive task in fatigue status is reflected as the enhancement of the small-worldness.

Selección de Canales en Sistemas BCI basados en Potenciales P300 mediante Inteligencia de Enjambre

Brain-Computer Interfaces (BCI) are direct communication pathways between the brain and the environment that translate certain features, which correspond to users’ intentions, into device control commands. Channel selection in BCI systems is essential to avoid over-fitting, to reduce the computational cost and to increase the users’ comfort. Although several algorithms have previously developed for that purpose, metaheuristics based on swarm intelligence have not been exploited yet in P300-based BCI systems. In this study, a comparative among five different swarm methods, based on the behavior of biological systems, is shown. Those methods have been applied in order to optimize the channel selection procedure in this kind of systems, and have been tested with the ‘III BCI Competition 2005’ database II. Results show that the five methods can achieve similar or even higher accuracies than that obtained without performing any channel selection procedure. Owing to the fact that all the applied methods are able to drastically reduce the required number of channels without compromising the system performance, as well as to overcome the common 8-channel set and the backward elimination algorithm, we conclude that all of them are suitable for use in the P300-BCI systems channel selection procedure.

An improved P300 pattern in BCI to catch user’s attention

Objective. Brain-computer interfaces (BCIs) can help patients who have lost control over most muscles but are still conscious and able to communicate or interact with the environment. One of the most popular types of BCI is the P300-based BCI. With this BCI, users are asked to count the number of appearances of target stimuli in an experiment. To date, the majority of visual P300-based BCI systems developed have used the same character or picture as the target for every stimulus presentation, which can bore users. Consequently, users attention may decrease or be negatively affected by adjacent stimuli. Approach. In this study, a new stimulus is presented to increase user concentration. Honeycomb-shaped figures with 1–3 red dots were used as stimuli. The number and the positions of the red dots in the honeycomb-shaped figure were randomly changed during BCI control. The user was asked to count the number of the dots presented in each flash instead of the number of times they flashed. To assess the performance of this new stimulus, another honeycomb-shaped stimulus, without red dots, was used as a control condition. Main results. The results showed that the honeycomb-shaped stimuli with red dots obtained significantly higher classification accuracies and information transfer rates (p < 0.05) compared to the honeycomb-shaped stimulus without red dots. Significance. The results indicate that this proposed method can be a promising approach to improve the performance of the BCI system and can be an efficient method in daily application.

Functional brain network and multichannel analysis for the P300-based brain computer interface system of lying detection

Deception is a complex cognition process which involves activities in different brain regions. However, most of the ERP based lie detection systems focus on the features of ERPs from few channels. In this study, we designed a multi-channel ERP based brain computer interface (BCI) system for lie detection. Based on this, two new EEG feature selection approaches, bootstrapped geometric difference (BGD) and network analysis were proposed and applied to feature recognition and classification system. Unlike other methods, our approaches focus on the changes of EEGs from different brain regions and the correlation between them. For the test, we focus on visual and auditory stimuli, two groups of subjects went through the test and their EEGs were recorded. For all subjects, BGD of the P300 for all the scalp electrodes combined with SVM classifier showed the average rate of recognition accuracy was 84.4% and 82.2% for visual and auditory modality respectively. Statistical analysis of network features indicated the difference in the two groups were significant and the average accuracy rate reached 88.7% and 83.5% respectively, and the guilty group showed more obvious small-world property than innocent group. The results suggest the BGD and network analysis based approaches combined with SVM are efficient for ERP based expert and intelligent system for detection and evaluation of deception. The combination of these methods and other feature selection approaches can promote the development and application of ERP based lie detection system.

Concentration on performance with P300-based BCI systems: A matter of interface features

People who suffer from severe motor disabilities have difficulties to communicate with others or to interact with their environment using natural, i.e., muscular channels. These limitations can be overcome to some extent by using brain–computer interfaces (BCIs), because such systems allow users to communicate on the basis of their brain activity only. Among the several types of BCIs for spelling purposes, those that rely on the P300 event related potential—P300-based spellers—are chosen preferentially due to their high reliability. However, they demand from the user to sustain his/her attention to the desired character over a relatively long period of time. Therefore, the user's capacity to concentrate can affect his/her performance with a P300-based speller. The aim of this study was to test this hypothesis using three different interfaces: one based on the classic P300 speller paradigm, another also based on that speller but including a word predictor, and a third one that was based on the T9 interface developed for mobile phones. User performance was assessed by measuring the time to complete a spelling task and the accuracy of character selection. The d2 test was applied to assess attention and concentration. Sample (N = 14) was divided into two groups basing on of concentration scores. As a result, performance was better with the predictor-enriched interfaces: less time was needed to solve the task and participants made fewer errors (p < .05). There were also significant effects of concentration (p < .05) on performance with the standard P300 speller. In conclusion, the performance of those users with lower concentration level can be improved by providing BCIs with more interactive interfaces. These findings provide substantial evidence in order to highlight the impact of psychological features on BCI performance and should be taken into account for future assistive technology systems.

The Human Factors and Ergonomics of P300-Based Brain-Computer Interfaces.

Individuals with severe neuromuscular impairments face many challenges in communication and manipulation of the environment. Brain-computer interfaces (BCIs) show promise in presenting real-world applications that can provide such individuals with the means to interact with the world using only brain waves. Although there has been a growing body of research in recent years, much relates only to technology, and not to technology in use—i.e., real-world assistive technology employed by users. This review examined the literature to highlight studies that implicate the human factors and ergonomics (HFE) of P300-based BCIs. We assessed 21 studies on three topics to speak directly to improving the HFE of these systems: (1) alternative signal evocation methods within the oddball paradigm; (2) environmental interventions to improve user performance and satisfaction within the constraints of current BCI systems; and (3) measures and methods of measuring user acceptance. We found that HFE is central to the performance of P300-based BCI systems, although researchers do not often make explicit this connection. Incorporation of measures of user acceptance and rigorous usability evaluations, increased engagement of disabled users as test participants, and greater realism in testing will help progress the advancement of P300-based BCI systems in assistive applications.

Experimental Set Up of P300 Based Brain Computer Interface Using a Bioamplifier and BCI2000 System for Patients with Spinal Cord Injury.

Objective Brain computer interface (BCI) is one of the most promising technologies for helping people with neurological disorders. Most current BCI systems are relatively expensive and difficult to set up. Therefore, we developed a P300-based BCI system with a cheap bioamplifier and open source software. The purpose of this study was to describe the setup process of the system and preliminary experimental results.MethodsTen spinal cord-injured patients were recruited. We used a sixteen-channel EEG(KT88-1016, Contec, China) and BCI2000 software (Wadsworth center, NY, USA). Subjects were asked to spell a 5-character word using the P300-based BCI system with 10 minutes of training. EEG data were acquired during the experiment. After subjects spelled the word for ten trials, the spelling accuracy and information transfer rate (ITR) were obtained in each patients.ResultsAll subjects performed the experiment without difficulty. The mean accuracy was 59.4±22.8%. The spelling accuracy reversely correlated with the age. Younger subjects spelled with higher accuracy than older subjects (p=0.018). However, sex, injury level, time since injury and ASIA scale were not correlated with the accuracy. The mean of ITR was 2.26±1.22 bit/min.ConclusionThis study showed that a BCI system can be set up inexpensively with a low-price bioamplifier and open-source software. The spelling accuracy was moderately achieved with our system. P300-based BCI is useful in young patients, but modification is necessary in old patients who have low ability of recognition and concentration.

A P300-based brain computer interface system for words typing

P300 is an event related potential of the brain in response to oddball events. Brain Computer Interface (BCI) utilizing P300 is known as a P300 BCI system. A conventional P300 BCI system for character spelling is composed of a paradigm that displays flashing characters and a classification scheme which identifies target characters. To type a word a user has to spell each character of the word: this spelling process is slow and it can take several minutes to type a word. In this study, we propose a new word typing scheme by integrating a word suggestion mechanism with a dictionary search into the conventional P300-based speller. Our new P300-based word typing system consists of an initial character spelling paradigm, a dictionary unit to give suggestions of possible words and the second word selection paradigm to select a word out of the suggestions. Our proposed methodology reduces typing time significantly and makes word typing easy via a P300 BCI system. We have tested our system with ten subjects and our results demonstrate an average word typing time of 1.91min whereas the conventional took 3.36min for the same words.

Comparison of Pre-Processing and Classification Techniques for Single-Trial and Multi-Trial P300-Based Brain Computer Interfaces

The P300 component of Event Related Brain Potentials (ERP) is commonly used in Brain Computer Interfaces (BCI) to translate the intentio ns of an individual into commands for external devices. The P300 response, however, resides in a s ignal environment of high background noise. Consequently, the main problem in developing a P300-based BCI lies in identifying the P300 response in the presence of this noise. Traditionally, atten uating the background activity of P300 data is done by averaging multiple trials of recorded signals. This method, though effective, suffers two drawbacks. First, collecting multiple trials of data is time c onsuming and delays the BCI response. Second, latency distortions may appear in the averaged result due t o variable time-locking of the P300 in the individu al trials. Problem statement: The use of single-trial P300 data overcomes both t hese shortcomings. However, single-trial data must be properly denoise d to allow for reliable BCI operation. Single-trial P300-based BCIs have been implemented using a variety of signal processing techniques and classification methodologies. However, comparing the accuracies of these systems to other multi-trial systems is likely to include the comparison of more than just the trial format (single-trial/multi-tri al) as the data quality and recording circumstances are li kely to be dissimilar. Approach: This issue was directly addressed by comparing the performance comparison of three different preprocessing agents and three classification methodologies on the same data set over both the single-trial and multi-trial settings. The P300 data set of BCI Competition II w as used to facilitate this comparison. Results: The LDA classifier exhibited the best performance in cl assifying unseen P300 spatiotemporal features in both the single-trial (74.19%) and multi-trial form at (100%). It is also very efficient in terms of computational and memory requirements. Conclusion: This study can serve as a general guide for practitioners developing single-trial and multi-tri al P300-based BCI systems, particularly for selecti ng appropriate pre-processing agents and classificatio n methodologies for inclusion. The possibilities fo r future study include the investigation of double-tr ial and triple-trial P300 system based on the LDA classifier. The time savings of such approaches wil l still be significant. It is very likely that such systems would benefit from accuracies higher than t he one obtained in this study for single-trial LDA (74.19%).

Evaluation of P300-Based Brain-Computer Interface in Real-World Contexts

Despite recent advances in brain-computer interface (BCI) development, system usability still remains a large oversight. The goal of this study was to investigate the usability of a P300-based BCI system, P300 Speller, by assessing how background noise and interface color contrast affect user performance and BCI usage preference. Fifteen able-bodied participants underwent a 2 (low and high interface color contrast) × 3 (low, medium, and high background noise level) within-subjects design experiment, in which participants were asked to type six 10-character phrases in the P300 Speller paradigm. The overall accuracy in the study was 80.2%. Participants showed higher accuracy, higher information transfer rate, bigger amplitude, and smaller latency in the high interface color contrast condition than in the low contrast condition. Participants had better performance in the noisy condition than in the quiet condition, but the background noise effects were not statistically significant in the present study. These results should give some insight to the real-world applicability of the current P300 Speller as a nonmuscular communication system, especially for individuals with severe neuromuscular disabilities.