Single trial detection of error-related potentials in brain-machine interfaces: a survey and comparison of methods.

Objective.Error-related potential (ErrP) is a potential elicited in the brain when humans perceive an error. ErrPs have been researched in a variety of contexts, such as to increase the reliability of brain-computer interfaces (BCIs), increase the naturalness of human-machine interaction systems, teach systems, as well as study clinical conditions. Still, there is a significant challenge in detecting ErrP from a single trial, which may hamper its effective use. The literature presents ErrP detection accuracies quite variable across studies, which raises the question of whether this variability depends more on classification pipelines or on the quality of elicited ErrPs (mostly directly related to the underlying paradigms).Approach.With this purpose, 11 datasets have been used to compare several classification pipelines which were selected according to the studies that reported online performance above 75%. We also analyze the effects of different steps of the pipelines, such as resampling, window selection, augmentation, feature extraction, and classification.Main results.From our analysis, we have found that shrinkage-regularized linear discriminant analysis is the most robust method for classification, and for feature extraction, using Fisher criterion beamformer spatial features and overlapped window averages result in better classification performance. The overall experimental results suggest that classification accuracy is highly dependent on user tasks in BCI experiments and on signal quality (in terms of ErrP morphology, signal-to-noise ratio (SNR), and discrimination).Significance.This study contributes to the BCI research field by responding to the need for a guideline that can direct researchers in designing ErrP-based BCI tasks by accelerating the design steps.

A new error-monitoring brain-computer interface based on reinforcement learning for people with autism spectrum disorders.

Objective.Brain-computer interfaces (BCIs) are emerging as promising cognitive training tools in neurodevelopmental disorders, as they combine the advantages of traditional computerized interventions with real-time tailored feedback. We propose a gamified BCI based on non-volitional neurofeedback for cognitive training, aiming at reaching a neurorehabilitation tool for application in autism spectrum disorders (ASDs).Approach.The BCI consists of an emotional facial expression paradigm controlled by an intelligent agent that makes correct and wrong actions, while the user observes and judges the agent's actions. The agent learns through reinforcement learning (RL) an optimal strategy if the participant generates error-related potentials (ErrPs) upon incorrect agent actions. We hypothesize that this training approach will allow not only the agent to learn but also the BCI user, by participating through implicit error scrutiny in the process of learning through operant conditioning, making it of particular interest for disorders where error monitoring processes are altered/compromised such as in ASD. In this paper, the main goal is to validate the whole methodological BCI approach and assess whether it is feasible enough to move on to clinical experiments. A control group of ten neurotypical participants and one participant with ASD tested the proposed BCI approach.Main results.We achieved an online balanced-accuracy in ErrPs detection of 81.6% and 77.1%, respectively for two different game modes. Additionally, all participants achieved an optimal RL strategy for the agent at least in one of the test sessions.Significance.The ErrP classification results and the possibility of successfully achieving an optimal learning strategy, show the feasibility of the proposed methodology, which allows to move towards clinical experimentation with ASD participants to assess the effectiveness of the approach as hypothesized.

Point Cloud Compression: Impact on Object Detection in Outdoor Contexts.

Increasing demand for more reliable and safe autonomous driving means that data involved in the various aspects of perception, such as object detection, will become more granular as the number and resolution of sensors progress. Using these data for on-the-fly object detection causes problems related to the computational complexity of onboard processing in autonomous vehicles, leading to a desire to offload computation to roadside infrastructure using vehicle-to-infrastructure communication links. The need to transmit sensor data also arises in the context of vehicle fleets exchanging sensor data, over vehicle-to-vehicle communication links. Some types of sensor data modalities, such as Light Detection and Ranging (LiDAR) point clouds, are so voluminous that their transmission is impractical without data compression. With most emerging autonomous driving implementations being anchored on point cloud data, we propose to evaluate the impact of point cloud compression on object detection. To that end, two different object detection architectures are evaluated using point clouds from the KITTI object dataset: raw point clouds and point clouds compressed with a state-of-the-art encoder and three different compression levels. The analysis is extended to the impact of compression on depth maps generated from images projected from the point clouds, with two conversion methods tested. Results show that low-to-medium levels of compression do not have a major impact on object detection performance, especially for larger objects. Results also show that the impact of point cloud compression is lower when detecting objects using depth maps, placing this particular method of point cloud data representation on a competitive footing compared to raw point cloud data.

Visuo-auditory stimuli with semantic, temporal and spatial congruence for a P300-based BCI: An exploratory test with an ALS patient in a completely locked-in state.

BACKGROUND: Brain-computer interfaces (BCIs) are a promising tool for communication with completely locked-in state (CLIS) patients. Despite the great efforts already made by the BCI research community, the cases of success are still very few, very exploratory, limited in time, and based on simple 'yes/no' paradigms. NEW METHOD: A P300-based BCI is proposed comparing two conditions, one corresponding to purely spatial auditory stimuli (AU-S) and the other corresponding to hybrid visual and spatial auditory stimuli (HVA-S). In the HVA-S condition, there is a semantic, temporal, and spatial congruence between visual and auditory stimuli. The stimuli comprise a lexicon of 7 written and spoken words. Spatial sounds are generated through the head-related transfer function. Given the good results obtained with 10 able-bodied participants, we investigated whether a patient entering CLIS could use the proposed BCI. RESULTS: The able-bodied group achieved 71.3 % and 90.5 % online classification accuracy for the auditory and hybrid BCIs respectively, while the patient achieved 30 % and chance level accuracies, for the same conditions. Notwithstanding, the patient's event-related potentials (ERPs) showed statistical discrimination between target and non-target events in different time windows. COMPARISON WITH EXISTING METHODS: The results of the control group compare favorably with the state-of-the-art, considering a 7-class BCI controlled visual-covertly and with auditory stimuli. The integration of visual and auditory stimuli has not been tested before with CLIS patients. CONCLUSIONS: The semantic, temporal, and spatial congruence of the stimuli increased the performance of the control group, but not of the CLIS patient, which can be due to impaired attention and cognitive function. The patient's unique ERP patterns make interpretation difficult, requiring further tests/paradigms to decouple patients' responses at different levels (reflexive, perceptual, cognitive). The ERPs discrimination found indicates that a simplification of the proposed approaches may be feasible.

Robust Event-Based Vision Model Estimation by Dispersion Minimisation.

We propose a novel Dispersion Minimisation framework for event-based vision model estimation, with applications to optical flow and high-speed motion estimation. The framework extends previous event-based motion compensation algorithms by avoiding computing an optimisation score based on an explicit image-based representation, which provides three main benefits: i) The framework can be extended to perform incremental estimation, i.e., on an event-by-event basis. ii) Besides purely visual transformations in 2D, the framework can readily use additional information, e.g., by augmenting the events with depth, to estimate the parameters of motion models in higher dimensional spaces. iii) The optimisation complexity only depends on the number of events. We achieve this by modelling the event alignment according to candidate parameters and minimising the resultant dispersion, which is computed by a family of suitable entropy-based measures. Data whitening is also proposed as a simple and effective pre-processing step to make the framework's accuracy performance more robust, as well as other event-based motion-compensation methods. The framework is evaluated on several challenging motion estimation problems, including 6-DOF transformation, rotational motion, and optical flow estimation, achieving state-of-the-art performance.

Cross-Subject Zero Calibration Driver's Drowsiness Detection: Exploring Spatiotemporal Image Encoding of EEG Signals for Convolutional Neural Network Classification.

This paper explores two methodologies for drowsiness detection using EEG signals in a sustained-attention driving task considering pre-event time windows, and focusing on cross-subject zero calibration. Driving accidents are a major cause of injuries and deaths on the road. A considerable portion of those are due to fatigue and drowsiness. Advanced driver assistance systems that could detect mental states which are associated with hazardous situations, such as drowsiness, are of critical importance. EEG signals are used widely for brain-computer interfaces, as well as mental state recognition. However, these systems are still difficult to design due to very low signal-to-noise ratios and cross-subject disparities, requiring individual calibration cycles. To tackle this research domain, here, we explore drowsiness detection based on EEG signals' spatiotemporal image encoding representations in the form of either recurrence plots or gramian angular fields for deep convolutional neural network (CNN) classification. Results comparing both techniques using a public dataset of 27 subjects show a superior balanced accuracy of up to 75.87% for leave-one-out cross-validation, using both techniques, against works in the literature, demonstrating the possibility to pursue cross-subject zero calibration design.

Detection of Stressful Situations Using GSR While Driving a BCI-controlled Wheelchair.

This paper analyzes the galvanic skin response (GSR) recorded from healthy and motor disabled people while steering a robotic wheelchair (RobChair ISR-UC prototype), to infer whether GSR can help in the recognition of stressful situations. Seven healthy individuals and six individuals with motor disabilities were asked to drive the RobChair by means of a brain-computer interface in indoor office environments, including complex scenarios such as passing narrow doors, avoiding obstacles, and with situations of unexpected trajectories of the wheelchair (controlled by an operator without users knowledge). All these driving situations can trigger emotional arousals such as anxiety and stress. A method called feature-based peak detection (FBPD) was proposed for automatic detection of skin conductance response (SCR) which proved to be very effective compared to the state-of-the-art methods. We found that SCR was elicited in 100% of the occurrences of collisions (lateral scrapings) and 94% of unexpected trajectories.

Double ErrP Detection for Automatic Error Correction in an ERP-Based BCI Speller.

Brain-computer interface (BCI) is a useful device for people with severe motor disabilities. However, due to its low speed and low reliability, BCI still has a very limited application in daily real-world tasks. This paper proposes a P300-based BCI speller combined with a double error-related potential (ErrP) detection to automatically correct erroneous decisions. This novel approach introduces a second error detection to infer whether wrong automatic correction also elicits a second ErrP. Thus, two single-trial responses, instead of one, contribute to the final selection, improving the reliability of error detection. Moreover, to increase error detection, the evoked potential detected as target by the P300 classifier is combined with the evoked error potential at a feature-level. Discriminable error and positive potentials (response to correct feedback) were clearly identified. The proposed approach was tested on nine healthy participants and one tetraplegic participant. The online average accuracy for the first and second ErrPs were 88.4% and 84.8%, respectively. With automatic correction, we achieved an improvement around 5% achieving 89.9% in spelling accuracy for an effective 2.92 symbols/min. The proposed approach revealed that double ErrP detection can improve the reliability and speed of BCI systems.

Pure visual imagery as a potential approach to achieve three classes of control for implementation of BCI in non-motor disorders.

OBJECTIVE: The achievement of multiple instances of control with the same type of mental strategy represents a way to improve flexibility of brain-computer interface (BCI) systems. Here we test the hypothesis that pure visual motion imagery of an external actuator can be used as a tool to achieve three classes of electroencephalographic (EEG) based control, which might be useful in attention disorders. APPROACH: We hypothesize that different numbers of imagined motion alternations lead to distinctive signals, as predicted by distinct motion patterns. Accordingly, a distinct number of alternating sensory/perceptual signals would lead to distinct neural responses as previously demonstrated using functional magnetic resonance imaging (fMRI). We anticipate that differential modulations should also be observed in the EEG domain. EEG recordings were obtained from twelve participants using three imagery tasks: imagery of a static dot, imagery of a dot with two opposing motions in the vertical axis (two motion directions) and imagery of a dot with four opposing motions in vertical or horizontal axes (four directions). The data were analysed offline. MAIN RESULTS: An increase of alpha-band power was found in frontal and central channels as a result of visual motion imagery tasks when compared with static dot imagery, in contrast with the expected posterior alpha decreases found during simple visual stimulation. The successful classification and discrimination between the three imagery tasks confirmed that three different classes of control based on visual motion imagery can be achieved. The classification approach was based on a support vector machine (SVM) and on the alpha-band relative spectral power of a small group of six frontal and central channels. Patterns of alpha activity, as captured by single-trial SVM closely reflected imagery properties, in particular the number of imagined motion alternations. SIGNIFICANCE: We found a new mental task based on visual motion imagery with potential for the implementation of multiclass (3) BCIs. Our results are consistent with the notion that frontal alpha synchronization is related with high internal processing demands, changing with the number of alternation levels during imagery. Together, these findings suggest the feasibility of pure visual motion imagery tasks as a strategy to achieve multiclass control systems with potential for BCI and in particular, neurofeedback applications in non-motor (attentional) disorders.

Importance Weighted Import Vector Machine for Unsupervised Domain Adaptation.

In real-world applications, the assumption of independent and identical distribution is no longer consistent. To alleviate the significant mismatch between source and target domains, importance weighting import vector machine, which is an adaptive classifier, is proposed. This adaptive probabilistic classification method, which is sparse and computationally efficient, can be used for unsupervised domain adaptation (DA). The effectiveness of the proposed approach is demonstrated via a toy problem, and a real-world cross-domain object recognition task. Even though the sparseness, the proposed method outperforms the state-of-the-art in both unsupervised and semisupervised DA scenarios. We also introduce a reliable importance weighted cross validation (RIWCV), which is an improvement of importance weighted cross validation, for parameter and model selection. The RIWCV avoid falling down in local minimum, by selecting a more reliable combination of the parameters instead of the best parameters.

Control of Brain Activity in hMT+/V5 at Three Response Levels Using fMRI-Based Neurofeedback/BCI.

A major challenge in brain-computer interface (BCI) research is to increase the number of command classes and levels of control. BCI studies often use binary control level approaches (level 0 and 1 of brain activation for each class of control). Different classes may often be achieved but not different levels of activation for the same class. The increase in the number of levels of control in BCI applications may allow for larger efficiency in neurofeedback applications. In this work we test the hypothesis whether more than two modulation levels can be achieved in a single brain region, the hMT+/V5 complex. Participants performed three distinct imagery tasks during neurofeedback training: imagery of a stationary dot, imagery of a dot with two opposing motions in the vertical axis and imagery of a dot with four opposing motions in vertical or horizontal axes (imagery of 2 or 4 motion directions). The larger the number of motion alternations, the higher the expected hMT+/V5 response. A substantial number (17 of 20) of participants achieved successful binary level of control and 12 were able to reach even 3 significant levels of control within the same session, confirming the whole group effects at the individual level. With this simple approach we suggest that it is possible to design a parametric system of control based on activity modulation of a specific brain region with at least 3 different levels. Furthermore, we show that particular imagery task instructions, based on different number of motion alternations, provide feasible achievement of different control levels in BCI and/or neurofeedback applications.

Probabilistic Social Behavior Analysis by Exploring Body Motion-Based Patterns.

Understanding human behavior through nonverbal-based features, is interesting in several applications such as surveillance, ambient assisted living and human-robot interaction. In this article in order to analyze human behaviors in social context, we propose a new approach which explores interrelations between body part motions in scenarios with people doing a conversation. The novelty of this method is that we analyze body motion-based features in frequency domain to estimate different human social patterns: Interpersonal Behaviors (IBs) and a Social Role (SR). To analyze the dynamics and interrelations of people's body motions, a human movement descriptor is used to extract discriminative features, and a multi-layer Dynamic Bayesian Network (DBN) technique is proposed to model the existent dependencies. Laban Movement Analysis (LMA) is a well-known human movement descriptor, which provides efficient mid-level information of human body motions. The mid-level information is useful to extract the complex interdependencies. The DBN technique is tested in different scenarios to model the mentioned complex dependencies. The study is applied for obtaining four IBs (Interest, Indicator, Empathy and Emphasis) to estimate one SR (Leading).The obtained results give a good indication of the capabilities of the proposed approach for people interaction analysis with potential applications in human-robot interaction.

ISRUC-Sleep: A comprehensive public dataset for sleep researchers.

To facilitate the performance comparison of new methods for sleep patterns analysis, datasets with quality content, publicly-available, are very important and useful. We introduce an open-access comprehensive sleep dataset, called ISRUC-Sleep. The data were obtained from human adults, including healthy subjects, subjects with sleep disorders, and subjects under the effect of sleep medication. Each recording was randomly selected between PSG recordings that were acquired by the Sleep Medicine Centre of the Hospital of Coimbra University (CHUC). The dataset comprises three groups of data: (1) data concerning 100 subjects, with one recording session per subject; (2) data gathered from 8 subjects; two recording sessions were performed per subject, and (3) data collected from one recording session related to 10 healthy subjects. The polysomnography (PSG) recordings, associated with each subject, were visually scored by two human experts. Comparing the existing sleep-related public datasets, ISRUC-Sleep provides data of a reasonable number of subjects with different characteristics such as: data useful for studies involving changes in the PSG signals over time; and data of healthy subjects useful for studies involving comparison of healthy subjects with the patients, suffering from sleep disorders. This dataset was created aiming to complement existing datasets by providing easy-to-apply data collection with some characteristics not covered yet. ISRUC-Sleep can be useful for analysis of new contributions: (i) in biomedical signal processing; (ii) in development of ASSC methods; and (iii) on sleep physiology studies. To evaluate and compare new contributions, which use this dataset as a benchmark, results of applying a subject-independent automatic sleep stage classification (ASSC) method on ISRUC-Sleep dataset are presented.

Toward a reliable gaze-independent hybrid BCI combining visual and natural auditory stimuli.

BACKGROUND: Brain computer interfaces (BCIs) are one of the last communication options for patients in the locked-in state (LIS). For complete LIS patients, interfaces must be gaze-independent due to their eye impairment. However, unimodal gaze-independent approaches typically present levels of performance substantially lower than gaze-dependent approaches. The combination of multimodal stimuli has been pointed as a viable way to increase users' performance. NEW METHOD: A hybrid visual and auditory (HVA) P300-based BCI combining simultaneously visual and auditory stimulation is proposed. Auditory stimuli are based on natural meaningful spoken words, increasing stimuli discrimination and decreasing user's mental effort in associating stimuli to the symbols. The visual part of the interface is covertly controlled ensuring gaze-independency. RESULTS: Four conditions were experimentally tested by 10 healthy participants: visual overt (VO), visual covert (VC), auditory (AU) and covert HVA. Average online accuracy for the hybrid approach was 85.3%, which is more than 32% over VC and AU approaches. Questionnaires' results indicate that the HVA approach was the less demanding gaze-independent interface. Interestingly, the P300 grand average for HVA approach coincides with an almost perfect sum of P300 evoked separately by VC and AU tasks. COMPARISON WITH EXISTING METHODS: The proposed HVA-BCI is the first solution simultaneously embedding natural spoken words and visual words to provide a communication lexicon. Online accuracy and task demand of the approach compare favorably with state-of-the-art. CONCLUSIONS: The proposed approach shows that the simultaneous combination of visual covert control and auditory modalities can effectively improve the performance of gaze-independent BCIs.

A two-step automatic sleep stage classification method with dubious range detection.

BACKGROUND: The limitations of the current systems of automatic sleep stage classification (ASSC) are essentially related to the similarities between epochs from different sleep stages and the subjects' variability. Several studies have already identified the situations with the highest likelihood of misclassification in sleep scoring. Here, we took advantage of such information to develop an ASSC system based on knowledge of subjects' variability of some indicators that characterize sleep stages and on the American Academy of Sleep Medicine (AASM) rules. METHODS: An ASSC system consisting of a two-step classifier is proposed. In the first step, epochs are classified using support vector machines (SVMs) spread into different nodes of a decision tree. In the post-processing step, the epochs suspected of misclassification (dubious classification) are tagged, and a new classification is suggested. Identification and correction are based on the AASM rules, and on misclassifications most commonly found/reported in automatic sleep staging. Six electroencephalographic and two electrooculographic channels were used to classify wake, non-rapid eye movement (NREM) sleep--N1, N2 and N3, and rapid eye movement (REM) sleep. RESULTS: The proposed system was tested in a dataset of 14 clinical polysomnographic records of subjects suspected of apnea disorders. Wake and REM epochs not falling in the dubious range, are classified with accuracy levels compatible with the requirements for clinical applications. The suggested correction assigned to the epochs that are tagged as dubious enhances the global results of all sleep stages. CONCLUSIONS: This approach provides reliable sleep staging results for non-dubious epochs.

Improving the generalization capacity of cascade classifiers.

The cascade classifier is a usual approach in object detection based on vision, since it successively rejects negative occurrences, e.g., background images, in a cascade structure, keeping the processing time suitable for on-the-fly applications. On the other hand, similar to other classifier ensembles, cascade classifiers are likely to have high Vapnik-Chervonenkis (VC) dimension, which may lead to overfitting the training data. Therefore, this work aims at improving the generalization capacity of the cascade classifier by controlling its complexity, which depends on the model of their classifier stages, the number of stages, and the feature space dimension of each stage, which can be controlled by integrating the parameter setting of the feature extractor (in our case an image descriptor) into the maximum-margin framework of support vector machine training, as will be shown in this paper. Moreover, to set the number of cascade stages, bounds on the false positive rate (FP) and on the true positive rate (TP) of cascade classifiers are derived based on a VC-style analysis. These bounds are applied to compose an enveloping receiver operating curve (EROC), i.e., a new curve in the TP­FP space in which each point is an ordered pair of upper bound on the FP and lower bound on the TP. The optimal number of cascade stages is forecasted by comparing EROCs of cascades with different numbers of stages.

A minimal solution for the extrinsic calibration of a camera and a laser-rangefinder.

This paper presents a new algorithm for the extrinsic calibration of a perspective camera and an invisible 2D laser-rangefinder (LRF). The calibration is achieved by freely moving a checkerboard pattern in order to obtain plane poses in camera coordinates and depth readings in the LRF reference frame. The problem of estimating the rigid displacement between the two sensors is formulated as one of registering a set of planes and lines in the 3D space. It is proven for the first time that the alignment of three plane-line correspondences has at most eight solutions that can be determined by solving a standard p3p problem and a linear system of equations. This leads to a minimal closed-form solution for the extrinsic calibration that can be used as hypothesis generator in a RANSAC paradigm. Our calibration approach is validated through simulation and real experiments that show the superiority with respect to the current state-of-the-art method requiring a minimum of five input planes.

Comparison of a row-column speller vs. a novel lateral single-character speller: assessment of BCI for severe motor disabled patients.

OBJECTIVE: Non-invasive brain-computer interface (BCI) based on electroencephalography (EEG) offers a new communication channel for people suffering from severe motor disorders. This paper presents a novel P300-based speller called lateral single-character (LSC). The LSC performance is compared to that of the standard row-column (RC) speller. METHODS: We developed LSC, a single-character paradigm comprising all letters of the alphabet following an event strategy that significantly reduces the time for symbol selection, and explores the intrinsic hemispheric asymmetries in visual perception to improve the performance of the BCI. RC and LSC paradigms were tested by 10 able-bodied participants, seven participants with amyotrophic lateral sclerosis (ALS), five participants with cerebral palsy (CP), one participant with Duchenne muscular dystrophy (DMD), and one participant with spinal cord injury (SCI). RESULTS: The averaged results, taking into account all participants who were able to control the BCI online, were significantly higher for LSC, 26.11 bit/min and 89.90% accuracy, than for RC, 21.91 bit/min and 88.36% accuracy. The two paradigms produced different waveforms and the signal-to-noise ratio was significantly higher for LSC. Finally, the novel LSC also showed new discriminative features. CONCLUSIONS: The results suggest that LSC is an effective alternative to RC, and that LSC still has a margin for potential improvement in bit rate and accuracy. SIGNIFICANCE: The high bit rates and accuracy of LSC are a step forward for the effective use of BCI in clinical applications.

Adaptive automatic sleep stage classification under covariate shift.

Current automatic sleep stage classification (ASSC) methods that rely on polysomnographic (PSG) signals suffer from inter-subject differences that make them unreliable in facing with new and different subjects. A novel adaptive sleep scoring method based on unsupervised domain adaptation, aiming to be robust to inter-subject variability, is proposed. We assume that the sleep quality variants follow a covariate shift model, where only the sleep features distribution change in the training and test phases. The maximum overlap discrete wavelet transform (MODWT) is applied to extract relevant features from EEG, EOG and EMG signals. A set of significant features are selected by minimum-redundancy maximum-relevance (mRMR) which is a powerful feature selection method. Finally, an instance-weighting method, namely the importance weighted kernel logistic regression (IWKLR) is applied for the purpose of obtaining adaptation in classification. The classification results using leave one out cross-validation (LOOCV), show that the proposed method performs at the state-of-the art in the field of ASSC.

GIBS block speller: toward a gaze-independent P300-based BCI.

Brain-computer interface (BCI) opens a new communication channel for individuals with severe motor disorders. In P300-based BCIs, gazing the target event plays an important role in the BCI performance. Individuals who have their eye movements affected may lose the ability to gaze targets that are in the visual periphery. This paper presents a novel P300-based paradigm called gaze independent block speller (GIBS), and compares its performance with that of the standard row-column (RC) speller. GIBS paradigm requires extra selections of blocks of letters. The online experiments made with able-bodied participants show that the users can effectively control GIBS without moving the eyes (covert attention), while this task is not possible with RC speller. Furthermore, with overt attention, the results show that the improved classification accuracy of GIBS over RC speller compensates the extra selections, thereby achieving similar practical bit rates.