Review of brain encoding and decoding mechanisms for EEG-based brain–computer interface

Xu, Lichao; Xu, Minpeng; Jung, Tzyy-Ping; Ming, Dong

doi:10.1007/s11571-021-09676-z

Cited by 51 publications

(23 citation statements)

References 178 publications

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“…During EEG signal processing, due to the individual differences between subjects, these hypotheses of machine learning cannot be fully met. Typically, this problem is addressed by calibrating the session for a long time to collect high-quality and large amounts of EEG data sets; however, this depends on subjects' cooperation and the quality of signal acquisition, greatly limiting the practical application of BCI [21][22][23]. To solve the cross-subject problem of transfer learning, in [24,25], the data in Riemannian space and European space, respectively, were aligned and transformed, which resulted in similar data distribution of different subjects, significantly improving the problem of transfer learning.…”

Section: Pre Processingmentioning

confidence: 99%

Motor Imagery EEG Classification Based on Transfer Learning and Multi-Scale Convolution Network

Chang

Zhang

2022

Micromachines

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For the successful application of brain-computer interface (BCI) systems, accurate recognition of electroencephalography (EEG) signals is one of the core issues. To solve the differences in individual EEG signals and the problem of less EEG data in classification and recognition, an attention mechanism-based multi-scale convolution network was designed; the transfer learning data alignment algorithm was then introduced to explore the application of transfer learning for analyzing motor imagery EEG signals. The data set 2a of BCI Competition IV was used to verify the designed dual channel attention module migration alignment with convolution neural network (MS-AFM). Experimental results showed that the classification recognition rate improved with the addition of the alignment algorithm and adaptive adjustment in transfer learning; the average classification recognition rate of nine subjects was 86.03%.

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Section: Pre Processingmentioning

confidence: 99%

Motor Imagery EEG Classification Based on Transfer Learning and Multi-Scale Convolution Network

Chang

Zhang

2022

Micromachines

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“…Alternatively, electroencephalography (EEG) allows for BCI devices to be tested directly among the target population. But because neural signals become distorted as they pass through the skull and scalp to reach EEG sensors 4 , BCI applications are often limited to the detection of stimulus-evoked potentials 5–8 , resulting in non-naturalistic paradigms that remain comparatively slow and inflexible 9,10 . A practical solution to speech decoding will require the quick and accurate classification of a large inventory of context-dependent speech sounds in rapid succession 11 .…”

Section: Background and Summarymentioning

confidence: 99%

An open-access EEG dataset for speech decoding: Exploring the role of articulation and coarticulation

Moreira

Carvalho

Mendes

et al. 2022

Preprint

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Electroencephalography (EEG) holds promise for brain-computer interface (BCI) devices as a non-invasive measure of neural activity. With increased attention to EEG-based BCI systems, publicly available datasets that can represent the complex tasks required for naturalistic speech decoding are necessary to establish a common standard of performance within the BCI community. Effective solutions must overcome various kinds of noise in the EEG signal and remain reliable across sessions and subjects without overfitting to a specific dataset or task. We present two validated datasets (N=8 and N=16) for classification at the phoneme and word level and by the articulatory properties of phonemes. EEG signals were recorded from 64 channels while subjects listened to and repeated six consonants and five vowels. Individual phonemes were combined in different phonetic environments to produce coarticulated variation in forty consonant-vowel pairs, twenty real words, and twenty pseudowords. Phoneme pairs and words were presented during a control condition and during transcranial magnetic stimulation targeted to inhibit or augment the EEG signal associated with specific articulatory processes.

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“…LCD monitor to present the visual stimuli (Ge et al, 2019;Chen et al, 2021;Xu et al, 2021). In addition, to the best of our knowledge, hybrid visual stimuli that consolidate PRS with periodic motions have never been proposed in previous studies.…”

Section: Introductionmentioning

confidence: 99%

“…Furthermore, the effect of the motion parameters (i.e., the waveform of the temporal motion dynamics) on the BCI performances has not been investigated. Indeed, the investigation of the performance of various GSS-like visual stimuli with the LCD monitor environment is important because most SSVEP-based BCI studies employ the LCD monitor to present the visual stimuli (Ge et al, 2019 ; Chen et al, 2021 ; Xu et al, 2021 ). In addition, to the best of our knowledge, hybrid visual stimuli that consolidate PRS with periodic motions have never been proposed in previous studies.…”

Section: Introductionmentioning

confidence: 99%

Novel hybrid visual stimuli incorporating periodic motions into conventional flickering or pattern-reversal visual stimuli for steady-state visual evoked potential-based brain-computer interfaces

Kwon

Hwang

Nam

et al. 2022

Front. Neuroinform.

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In this study, we proposed a new type of hybrid visual stimuli for steady-state visual evoked potential (SSVEP)-based brain-computer interfaces (BCIs), which incorporate various periodic motions into conventional flickering stimuli (FS) or pattern reversal stimuli (PRS). Furthermore, we investigated optimal periodic motions for each FS and PRS to enhance the performance of SSVEP-based BCIs. Periodic motions were implemented by changing the size of the stimulus according to four different temporal functions denoted by none, square, triangular, and sine, yielding a total of eight hybrid visual stimuli. Additionally, we developed the extended version of filter bank canonical correlation analysis (FBCCA), which is a state-of-the-art training-free classification algorithm for SSVEP-based BCIs, to enhance the classification accuracy for PRS-based hybrid visual stimuli. Twenty healthy individuals participated in the SSVEP-based BCI experiment to discriminate four visual stimuli with different frequencies. An average classification accuracy and information transfer rate (ITR) were evaluated to compare the performances of SSVEP-based BCIs for different hybrid visual stimuli. Additionally, the user's visual fatigue for each of the hybrid visual stimuli was also evaluated. As the result, for FS, the highest performances were reported when the periodic motion of the sine waveform was incorporated for all window sizes except for 3 s. For PRS, the periodic motion of the square waveform showed the highest classification accuracies for all tested window sizes. A significant statistical difference in the performance between the two best stimuli was not observed. The averaged fatigue scores were reported to be 5.3 ± 2.05 and 4.05 ± 1.28 for FS with sine-wave periodic motion and PRS with square-wave periodic motion, respectively. Consequently, our results demonstrated that FS with sine-wave periodic motion and PRS with square-wave periodic motion could effectively improve the BCI performances compared to conventional FS and PRS. In addition, thanks to its low visual fatigue, PRS with square-wave periodic motion can be regarded as the most appropriate visual stimulus for the long-term use of SSVEP-based BCIs, particularly for window sizes equal to or larger than 2 s.

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Review of brain encoding and decoding mechanisms for EEG-based brain–computer interface

Cited by 51 publications

References 178 publications

Motor Imagery EEG Classification Based on Transfer Learning and Multi-Scale Convolution Network

Motor Imagery EEG Classification Based on Transfer Learning and Multi-Scale Convolution Network

An open-access EEG dataset for speech decoding: Exploring the role of articulation and coarticulation

Novel hybrid visual stimuli incorporating periodic motions into conventional flickering or pattern-reversal visual stimuli for steady-state visual evoked potential-based brain-computer interfaces

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