Prognosis for patients with cognitive motor dissociation identified by brain-computer interface.

Cognitive motor dissociation describes a subset of patients with disorders of consciousness who show neuroimaging evidence of consciousness but no detectable command-following behaviours. Although essential for family counselling, decision-making, and the design of rehabilitation programmes, the prognosis for patients with cognitive motor dissociation remains under-investigated. The current study included 78 patients with disorders of consciousness who showed no detectable command-following behaviours. These patients included 45 patients with unresponsive wakefulness syndrome and 33 patients in a minimally conscious state, as diagnosed using the Coma Recovery Scale-Revised. Each patient underwent an EEG-based brain-computer interface experiment, in which he or she was instructed to perform an item-selection task (i.e. select a photograph or a number from two candidates). Patients who achieved statistically significant brain-computer interface accuracies were identified as cognitive motor dissociation. Two evaluations using the Coma Recovery Scale-Revised, one before the experiment and the other 3 months later, were carried out to measure the patients' behavioural improvements. Among the 78 patients with disorders of consciousness, our results showed that within the unresponsive wakefulness syndrome patient group, 15 of 18 patients with cognitive motor dissociation (83.33%) regained consciousness, while only five of the other 27 unresponsive wakefulness syndrome patients without significant brain-computer interface accuracies (18.52%) regained consciousness. Furthermore, within the minimally conscious state patient group, 14 of 16 patients with cognitive motor dissociation (87.5%) showed improvements in their Coma Recovery Scale-Revised scores, whereas only four of the other 17 minimally conscious state patients without significant brain-computer interface accuracies (23.53%) had improved Coma Recovery Scale-Revised scores. Our results suggest that patients with cognitive motor dissociation have a better outcome than other patients. Our findings extend current knowledge of the prognosis for patients with cognitive motor dissociation and have important implications for brain-computer interface-based clinical diagnosis and prognosis for patients with disorders of consciousness.

BioPyC, an Open-Source Python Toolbox for Offline Electroencephalographic and Physiological Signals Classification.

Research on brain-computer interfaces (BCIs) has become more democratic in recent decades, and experiments using electroencephalography (EEG)-based BCIs has dramatically increased. The variety of protocol designs and the growing interest in physiological computing require parallel improvements in processing and classification of both EEG signals and bio signals, such as electrodermal activity (EDA), heart rate (HR) or breathing. If some EEG-based analysis tools are already available for online BCIs with a number of online BCI platforms (e.g., BCI2000 or OpenViBE), it remains crucial to perform offline analyses in order to design, select, tune, validate and test algorithms before using them online. Moreover, studying and comparing those algorithms usually requires expertise in programming, signal processing and machine learning, whereas numerous BCI researchers come from other backgrounds with limited or no training in such skills. Finally, existing BCI toolboxes are focused on EEG and other brain signals but usually do not include processing tools for other bio signals. Therefore, in this paper, we describe BioPyC, a free, open-source and easy-to-use Python platform for offline EEG and biosignal processing and classification. Based on an intuitive and well-guided graphical interface, four main modules allow the user to follow the standard steps of the BCI process without any programming skills: (1) reading different neurophysiological signal data formats, (2) filtering and representing EEG and bio signals, (3) classifying them, and (4) visualizing and performing statistical tests on the results. We illustrate BioPyC use on four studies, namely classifying mental tasks, the cognitive workload, emotions and attention states from EEG signals.

Measures of resting state EEG rhythms for clinical trials in Alzheimer's disease: Recommendations of an expert panel.

The Electrophysiology Professional Interest Area (EPIA) and Global Brain Consortium endorsed recommendations on candidate electroencephalography (EEG) measures for Alzheimer's disease (AD) clinical trials. The Panel reviewed the field literature. As most consistent findings, AD patients with mild cognitive impairment and dementia showed abnormalities in peak frequency, power, and "interrelatedness" at posterior alpha (8-12 Hz) and widespread delta (< 4 Hz) and theta (4-8 Hz) rhythms in relation to disease progression and interventions. The following consensus statements were subscribed: (1) Standardization of instructions to patients, resting state EEG (rsEEG) recording methods, and selection of artifact-free rsEEG periods are needed; (2) power density and "interrelatedness" rsEEG measures (e.g., directed transfer function, phase lag index, linear lagged connectivity, etc.) at delta, theta, and alpha frequency bands may be use for stratification of AD patients and monitoring of disease progression and intervention; and (3) international multisectoral initiatives are mandatory for regulatory purposes.

Face Representations via Tensorfaces of Various Complexities.

Neurons selective for faces exist in humans and monkeys. However, characteristics of face cell receptive fields are poorly understood. In this theoretical study, we explore the effects of complexity, defined as algorithmic information (Kolmogorov complexity) and logical depth, on possible ways that face cells may be organized. We use tensor decompositions to decompose faces into a set of components, called tensorfaces, and their associated weights, which can be interpreted as model face cells and their firing rates. These tensorfaces form a high-dimensional representation space in which each tensorface forms an axis of the space. A distinctive feature of the decomposition algorithm is the ability to specify tensorface complexity. We found that low-complexity tensorfaces have blob-like appearances crudely approximating faces, while high-complexity tensorfaces appear clearly face-like. Low-complexity tensorfaces require a larger population to reach a criterion face reconstruction error than medium- or high-complexity tensorfaces, and thus are inefficient by that criterion. Low-complexity tensorfaces, however, generalize better when representing statistically novel faces, which are faces falling beyond the distribution of face description parameters found in the tensorface training set. The degree to which face representations are parts based or global forms a continuum as a function of tensorface complexity, with low and medium tensorfaces being more parts based. Given the computational load imposed in creating high-complexity face cells (in the form of algorithmic information and logical depth) and in the absence of a compelling advantage to using high-complexity cells, we suggest face representations consist of a mixture of low- and medium-complexity face cells.

BCI-Based Rehabilitation on the Stroke in Sequela Stage.

Background: Stroke is the leading cause of serious and long-term disability worldwide. Survivors may recover some motor functions after rehabilitation therapy. However, many stroke patients missed the best time period for recovery and entered into the sequela stage of chronic stroke. Method: Studies have shown that motor imagery- (MI-) based brain-computer interface (BCI) has a positive effect on poststroke rehabilitation. This study used both virtual limbs and functional electrical stimulation (FES) as feedback to provide patients with a closed-loop sensorimotor integration for motor rehabilitation. An MI-based BCI system acquired, analyzed, and classified motor attempts from electroencephalogram (EEG) signals. The FES system would be activated if the BCI detected that the user was imagining wrist dorsiflexion on the instructed side of the body. Sixteen stroke patients in the sequela stage were randomly assigned to a BCI group and a control group. All of them participated in rehabilitation training for four weeks and were assessed by the Fugl-Meyer Assessment (FMA) of motor function. Results: The average improvement score of the BCI group was 3.5, which was higher than that of the control group (0.9). The active EEG patterns of the four patients in the BCI group whose FMA scores increased gradually became centralized and shifted to sensorimotor areas and premotor areas throughout the study. Conclusions: Study results showed evidence that patients in the BCI group achieved larger functional improvements than those in the control group and that the BCI-FES system is effective in restoring motor function to upper extremities in stroke patients. This study provides a more autonomous approach than traditional treatments used in stroke rehabilitation.

Adrenal metastases - long-term results of surgical treatment, single-centre experience.

INTRODUCTION: The adrenal gland is a frequent site of metastases in different types of cancer. The aim of this study was to assess the results of metastatic adrenalectomy in a single institution and to identify factors for survival. MATERIAL AND METHODS: A retrospective, single-centre analysis of outcomes of 39 patients (22 male, 17 female) with adrenal metastases who underwent surgery within 14 years (2004-2017) was performed. The median age at the time of adrenal surgery was 64.8 years (range 49-79 years). RESULTS: In our study group non-small cell lung cancer (NSCLC) was the most frequent primary tumour type (15 pts), followed by renal cell carcinoma (RCC) (14 pts) and colon cancer (6 pts). Most of the metastases - 36 (92%) - were metachronous (> 6 months). All synchronous metastases were NSCLC. The mean time from primary cancer to adrenalectomy was 42.3 months (range 1-176) and was statistically longer for RCC. In 3 patients (8%) metastases were bilateral and both adrenal glands were removed. In all patients, surgery was limited to the adrenal gland, and no major complications of surgery were observed. The median overall survival after metastasectomy was 18 months (3-81) and was statistically longer for colon cancer - 29.5 months (p = 0.012). In patients who died, tumours were significantly bigger than in survivors, 76.5 mm vs. 52.5 mm (p = 0.026). CONCLUSIONS: Surgery for adrenal metastasis is safe and indications for this procedure should be individualized. In selected patients, surgical removal of adrenal metastasis was associated with longer survival.

The Effects of Audiovisual Inputs on Solving the Cocktail Party Problem in the Human Brain: An fMRI Study.

At cocktail parties, our brains often simultaneously receive visual and auditory information. Although the cocktail party problem has been widely investigated under auditory-only settings, the effects of audiovisual inputs have not. This study explored the effects of audiovisual inputs in a simulated cocktail party. In our fMRI experiment, each congruent audiovisual stimulus was a synthesis of 2 facial movie clips, each of which could be classified into 1 of 2 emotion categories (crying and laughing). Visual-only (faces) and auditory-only stimuli (voices) were created by extracting the visual and auditory contents from the synthesized audiovisual stimuli. Subjects were instructed to selectively attend to 1 of the 2 objects contained in each stimulus and to judge its emotion category in the visual-only, auditory-only, and audiovisual conditions. The neural representations of the emotion features were assessed by calculating decoding accuracy and brain pattern-related reproducibility index based on the fMRI data. We compared the audiovisual condition with the visual-only and auditory-only conditions and found that audiovisual inputs enhanced the neural representations of emotion features of the attended objects instead of the unattended objects. This enhancement might partially explain the benefits of audiovisual inputs for the brain to solve the cocktail party problem.

A Hybrid System for Distinguishing between Brain Death and Coma Using Diverse EEG Features.

Electroencephalography (EEG) signals may provide abundant information reflecting the developmental changes in brain status. It usually takes a long time to finally judge whether a brain is dead, so an effective pre-test of brain states method is needed. In this paper, we present a hybrid processing pipeline to differentiate brain death and coma patients based on canonical correlation analysis (CCA) of power spectral density, complexity features, and feature fusion for group analysis. In addition, time-varying power spectrum and complexity were observed based on the analysis of individual patients, which can be used to monitor the change of brain status over time. Results showed three major differences between brain death and coma groups of EEG signal: slowing, increased complexity, and the improvement on classification accuracy with feature fusion. To the best of our knowledge, this is the first scheme for joint general analysis and time-varying state monitoring. Delta-band relative power spectrum density and permutation entropy could effectively be regarded as potential features of discrimination analysis on brain death and coma patients.

Surgical Management of Patients with Neuroendocrine Neoplasms of the Appendix: Appendectomy or More.

BACKGROUND: Appendiceal neuroendocrine neoplasms (ANEN) are mostly indolent tumours treated effectively with simple appendectomy. However, controversy exists regarding the necessity of oncologic right hemicolectomy (RH) in patients with histologic features suggestive of more aggressive disease. We assess the effects of current guidelines in selecting the surgical strategy (appendectomy or RH) for the management of ANEN. Methods/Aims: This is a retrospective review of all ANEN cases treated over a 14-year period at 3 referral centres and their management according to consensus guidelines of the European and the North American Neuroendocrine Tumor Societies (ENETS and NANETS, respectively). The operation performed, the tumour stage and grade, the extent of residual disease, and the follow-up outcomes were evaluated. RESULTS: Of 14,850 patients who had appendectomies, 215 (1.45%) had histologically confirmed ANEN. Four patients had synchronous non-ANEN malignancies. One hundred and ninety-three patients had index appendectomy. Seventeen patients (7.9%) had lymph node metastases within the mesoappendix. Forty-nine patients underwent RH after appendectomy. The percentages of 30-day morbidity and mortality after RH were 2 and 0%, respectively. Twelve patients (24.5%) receiving completion RH were found to have lymph node metastases. Two patients had liver metastases, both of them synchronous. The median follow-up was 38.5 months (range 1-143). No patient developed disease recurrence. Five- and 10-year overall survival for all patients with ANEN as the only malignancy was both 99.05%. CONCLUSIONS: The current guidelines appear effective in identifying ANEN patients at risk of harbouring nodal disease, but they question the oncological relevance of ANEN lymph node metastases. RH might present an overtreatment for a number of patients with ANEN.

Information Theoretic Approaches for Motor-Imagery BCI Systems: Review and Experimental Comparison.

Brain computer interfaces (BCIs) have been attracting a great interest in recent years. The common spatial patterns (CSP) technique is a well-established approach to the spatial filtering of the electroencephalogram (EEG) data in BCI applications. Even though CSP was originally proposed from a heuristic viewpoint, it can be also built on very strong foundations using information theory. This paper reviews the relationship between CSP and several information-theoretic approaches, including the Kullback-Leibler divergence, the Beta divergence and the Alpha-Beta log-det (AB-LD)divergence. We also revise other approaches based on the idea of selecting those features that are maximally informative about the class labels. The performance of all the methods will be also compared via experiments.

[Leiomyosarcoma of the adrenal gland - two cases report]. / Miesniakomiesak gladkokomórkowy nadnercza ­ opis dwóch przypadków.

Primary adrenal leiomyosarcomas are very rare adrenal mesenchymal tumours, only few dozen has been reported in the English language literature so far. We present two cases of patients with primary adrenal leiomyoosarcomas. 80 year-old female admitted with left adrenal tumour with presumptive diagnosis of nonadenoma, laparoscopic adrenalectomy was performed and 85 year-old man who underwent surgical resection by laparotomy of adrenal tumour suspected for adrenal carcinoma. Native density on unenhanced CT was 30 (HU) and 28 (HU) respectively and absolute and relative contrast washout was non-characteristic. Pathologic report reviled thinly encapsulated tumours composed of neoplastic cells with strong cytoplasmic reactivity for smooth muscle actin, desmin and vimentin. Male patient 20 months after surgery was diagnosed with multiple nonresectable metastases, systemic treatment was introduced. Female patient 28 months after surgery has no signs and symptoms of recurrence. We describe the clinical course and a brief review of clinical and histological features , biologic behaviour and diagnostic and therapeutic strategies. The extremely rare occurrence of primary adrenal leiomyosarcoma and a scientific literature based on single cases present challenges in establishing any firm conclusions on management and prognosis. Obviously it's impossible to have proper diagnosis based on clinical symptoms and diagnostic imaging. Radical surgery with free margins seems to be treatment of choice.

[Adrenal gangliuoneuroma - features of 10 cases in own material]. / Adrenal gangliuoneuroma ­ features of 10 cases in own material.

Adrenal ganglioneuroma is a rare benign neuroblastic tumor. Most of adrenal ganglioneuromas are asymptomatic and discovered incidentally during imaging examinations performed for many different indications. Proper preoperative diagnosis is challenging and most of this masses are described as being poor lipid adenomas or pheochromocytomas. AIM: The aim of the study was to present experience of referral center with this rare adrenal pathologies. MATERIALS AND METHODS: Analysis of our data - 374 adrenalectomies performed over last 13 years (2004-2016) in patients referred to our department from different endocrinological centers. Date of all patients were reviewed retrospectively in order to focus on adrenal ganlioneuroma. RESULTS: Among this patients histopathological report confirmed 10 adrenal ganglioneuromas (2,67% of cases, median age 51 years, range 39-74), diagnosed in 4 men and 6 women. Preoperative CT tumors were described as homogenous masses with mean attenuation on unenhanced images 28 (19-39 HU). In 5 patients (50%), progressive enhancement on delayed-phase postcontrast imaging was observed. 7 right and 3 left adrenalectomies was performed (laparoscopic approach in 50% of cases) Mean size of the resected tumors in histopatogical report was 47mm ( 5-85 ). CONCLUSIONS: Proper preoperative diagnosis of adrenal ganglioneuroma is challenging. In our series all diagnosis was made by histopathological examination. Surgery is indicated because it's difficult to distinguish adrenal gangioneuroma from other adrenal malignances. Radical excision is a definitive cure and may be done safely by laparoscopy.

[Twenty years long experience in the breast conserving treatment of women with preinvasive breast cancer. Evaluation of long term results and analysis of prognostic factors.] / Dwadziescia lat doswiadczen w leczeniu oszczedzajacym u kobiet chorych na przedinwazyjnego raka gruczolu piersiowego. Ocena odleglych wyników i analiza czynników rokowniczych.

AIM: The aim of the study was to analyze long term results and prognostic factors in women with preinvasive breast cancer (DCIS - ductal carcinoma in situ) who underwent breast conserving surgery with subsequent radiotherapy. MATERIALS AND METHODS: A total number of 106 patients was analyzed aged 29-78 years; mean age was 54,3 years and median 55 years. In 78 (73,5%) patients the tumor was diagnosed incidentally on mammography or ultrasound scan, 28 (26,5%) had palpable lesion. 57 patients had the tumor in the left breast and 49 in the right one. Most often the tumor was localized in external quadrants, namely in 56 (52,8%) patients. All patients had breast conserving surgery and then adjuvant radiotherapy of the breast in typical doses. For evaluation of survival we used the Kaplan-Meier test and for evaluation of cumulated loco-regional recurrence we have applied the method of competing risks. RESULTS: At present 101 patients are still alive, 85 have no relapse. 15 patients had local recurrence and 8 had another cancer. Five patients died during follow-up period. Overall 15-years survival in analyzed group was 88% and disease free survival was 74%. Of all prognostic factors only the value of Van Nuys index was relevant. Patients who had the index value less than 7 had significantly worse prognosis than patients with value 7 or more (p=0,043). CONCLUSIONS: At present 101 patients are still alive, 85 have no relapse. 15 patients had local recurrence and 8 had another cancer. Five patients died during follow-up period. Overall 15-years survival in analyzed group was 88% and disease free survival was 74%. Of all prognostic factors only the value of Van Nuys index was relevant. Patients who had the index value less than 7 had significantly worse prognosis than patients with value 7 or more (p=0,043).

Disseminated Pancreatic Neuroendocrine Neoplasm (NEN) with an Uncommon Localisation in the Central Nervous System. A Case Report.

BACKGROUND: Neuroendocrine neoplasms (NEN) are rare neoplasms that originate from neuroendocrine cells and are characterized by the potential of hormonal activity. Approximately 70% of these tumours are located in the gastrointestinal system (GI), followed by the bronchi, endocrine glands-like C cells of the thyroid (medullary carcinoma), the parasympathetic and sympathetic system (paragangliomas, pheochromocytoma) and other very rare locations. The prevalence of cerebral metastases in neuroendocrine tumours is estimated by various authors to be approximately 1.5-5%. When the primary tumour is located in the pancreas, it is associated with a risk of cerebral metastases lower than 2%. CASE REPORT: We describe a patient with a disseminated pancreatic NEN that presented with an isolated lesion in the brain. We gathered the important data via medical history,, observation, analysis of medical records, imaging and others diagnostic tests. Despite the fairly rare prevalence of cerebral metastases in NENs, a neurological work-up should be performed. This should include neuroimaging of the brain, preferably with MR, together with the somatostatin receptor scintigraphy (SRS), in each clinically suspicious case. A histopathological examination of the CNS tumour can confirm a dedifferentiation of NEN in the direction of a neuroendocrine carcinoma (NEC - neuroednocrine carcinoma) with a poor prognosis. CONCLUSIONS: Cerebral metastases are diagnosed in 1.5-5% of patients with a neuroendocrine neoplasm. In each case suggestive of a dissemination into the central nervous system, MRI of the brain should be performed.

Glutaminases in slowly proliferating gastroenteropancreatic neuroendocrine neoplasms/tumors (GEP-NETs): Selective overexpression of mRNA coding for the KGA isoform.

Glutamine (Gln) is a crucial metabolite in cancer cells of different origin, and the expression and activity of different isoforms of the Gln-degrading enzyme, glutaminase (GA), have variable implications for tumor growth and metabolism. Human glutaminases are encoded by two genes: the GLS gene encodes the kidney-type glutaminases, KGA and GAC, while the GLS2 gene encodes the liver-type glutaminases, GAB and LGA. Recent studies suggest that the GAC isoform and thus high GAC/KGA ratio, are characteristic of highly proliferating tumors, while GLS2 proteins have an inhibitory effect on tumor growth. Here we analyzed the expression levels of distinct GA transcripts in 7 gastroenteropancreatic neuroendocrine tumors (GEP-NETs) with low proliferation index and 7 non-neoplastic tissues. GEP-NETs overexpressed KGA, while GAC, which was the most abundant isoform, was not different from control. The expression of the GLS2 gene showed tendency towards elevation in GEP-NETs compared to control. Collectively, the expression pattern of GA isoforms conforms to the low proliferative capacity of GEP-NETs encompassed in this study.

Usefulness of Somatostatin Receptor Scintigraphy (Tc-[HYNIC, Tyr3]-Octreotide) and 123I-Metaiodobenzylguanidine Scintigraphy in Patients with SDHx Gene-Related Pheochromocytomas and Paragangliomas Detected by Computed Tomography.

AIMS: The aim of this study was to assess the usefulness of somatostatin receptor scintigraphy (SRS) using (99m)Tc-[HYNIC, Tyr3]-octreotide (TOC) and 123I-metaiodobenzylguanidine (mIBG) in patients with SDHx-related syndromes in which paragangliomas were detected by computed tomography and to establish an optimal imaging diagnostic algorithm in SDHx mutation carriers. METHODS: All carriers with clinical and radiological findings suggesting paragangliomas were screened by SRS and 123I-mIBG. Lesions were classified by body regions, i.e. head and neck, chest, abdomen with pelvis and adrenal gland as well as metastasis. RESULTS: We evaluated 46 SDHx gene mutation carriers (32 index cases and 14 relatives; 28 SDHD, 16 SDHB and 2 SDHC). In this group, 102 benign tumors were found in 39 studied patients, and malignant disease was diagnosed in 7 patients. In benign tumors, the sensitivity of SRS was estimated at 77% and of 123I-mIBG at 22.0%. The SRS and mIBG sensitivity was found to be clearly region dependent (p < 0.001). The highest SRS sensitivity was found in head and neck paragangliomas (HNP; 91.4%) and the lowest was found in abdominal paragangliomas and pheochromocytomas (40 and 42.9%, respectively). The highest 123I-mIBG sensitivity was found in pheochromocytomas (sensitivity of 100%) and the lowest in HNP (sensitivity of 3.7%). In metastatic disease, SRS was superior to mIBG (sensitivity of 95.2 vs. 23.8%, respectively). CONCLUSION: SRS and 123I-mIBG single photon emission computed tomography (SPECT) sensitivity in SDHx patients is highly body region dependent. In malignant tumors, SRS is superior to 123I-mIBG SPECT.

A auto-segmented multi-time window dual-scale neural network for brain-computer interfaces based on event-related potentials.

OBJECTIVE: Event-related potentials (ERPs) are cerebral responses to cognitive processes, also referred to as cognitive potentials. Accurately decoding ERPs can help to advance research on brain-computer interfaces (BCIs). The spatial pattern of ERP varies with time. In recent years, convolutional neural networks (CNNs) have shown promising results in electroencephalography (EEG) classification, specifically for ERP-based BCIs. APPROACH: This study proposes an auto-segmented multi-time window dual-scale neural network (AWDSNet). The combination of a multi-window design and a lightweight base network gives AWDSNet good performance at an acceptable cost of computing. For each individual, we create a time window set by calculating the correlation of signed R-squared values, which enables us to determine the length and number of windows automatically. The signal data are segmented based on the obtained window sets in sub-plus-global mode. Then, the multi-window data are fed into a dual-scale CNN model, where the sizes of the convolution kernels are determined by the window sizes. The use of dual-scale spatiotemporal convolution focuses on feature details while also having a large enough receptive length, and the grouping parallelism undermines the increase in the number of parameters that come with dual scaling. MAIN RESULTS: We evaluated the performance of AWDSNet on a public dataset and a self-collected dataset. A comparison was made with four popular methods including EEGNet, DeepConvNet, EEG-Inception, and PPNN. The experimental results show that AWDSNet has excellent classification performance with acceptable computational complexity. SIGNIFICANCE: These results indicate that AWDSNet has great potential for applications in ERP decoding.

MOCNN: A Multiscale Deep Convolutional Neural Network for ERP-Based Brain-Computer Interfaces.

Event-related potentials (ERPs) reflect neurophysiological changes of the brain in response to external events and their associated underlying complex spatiotemporal feature information is governed by ongoing oscillatory activity within the brain. Deep learning methods have been increasingly adopted for ERP-based brain-computer interfaces (BCIs) due to their excellent feature representation abilities, which allow for deep analysis of oscillatory activity within the brain. Features with higher spatiotemporal frequencies usually represent detailed and localized information, while features with lower spatiotemporal frequencies usually represent global structures. Mining EEG features from multiple spatiotemporal frequencies is conducive to obtaining more discriminative information. A multiscale feature fusion octave convolution neural network (MOCNN) is proposed in this article. MOCNN divides the ERP signals into high-, medium-and low-frequency components corresponding to different resolutions and processes them in different branches. By adding mid-and low-frequency components, the feature information used by MOCNN can be enriched, and the required amount of calculations can be reduced. After successive feature mapping using temporal and spatial convolutions, MOCNN realizes interactive learning among different components through the exchange of feature information among branches. Classification is accomplished by feeding the fused deep spatiotemporal features from various components into a fully connected layer. The results, obtained on two public datasets and a self-collected ERP dataset, show that MOCNN can achieve state-of-the-art ERP classification performance. In this study, the generalized concept of octave convolution is introduced into the field of ERP-BCI research, which allows effective spatiotemporal features to be extracted from multiscale networks through branch width optimization and information interaction at various scales.

A Time-Local Weighted Transformation Recognition Framework for Steady State Visual Evoked Potentials Based Brain-Computer Interfaces.

Canonical correlation analysis (CCA), Multivariate synchronization index (MSI), and their extended methods have been widely used for target recognition in Brain-computer interfaces (BCIs) based on Steady State Visual Evoked Potentials (SSVEP), and covariance calculation is an important process for these algorithms. Some studies have proved that embedding time-local information into the covariance can optimize the recognition effect of the above algorithms. However, the optimization effect can only be observed from the recognition results and the improvement principle of time-local information cannot be explained. Therefore, we propose a time-local weighted transformation (TT) recognition framework that directly embeds the time-local information into the electroencephalography signal through weighted transformation. The influence mechanism of time-local information on the SSVEP signal can then be observed in the frequency domain. Low-frequency noise is suppressed on the premise of sacrificing part of the SSVEP fundamental frequency energy, the harmonic energy of SSVEP is enhanced at the cost of introducing a small amount of high-frequency noise. The experimental results show that the TT recognition framework can significantly improve the recognition ability of the algorithms and the separability of extracted features. Its enhancement effect is significantly better than the traditional time-local covariance extraction method, which has enormous application potential.

A cross-dataset adaptive domain selection transfer learning framework for motor imagery-based brain-computer interfaces.

Objective. In brain-computer interfaces (BCIs) that utilize motor imagery (MI), minimizing calibration time has become increasingly critical for real-world applications. Recently, transfer learning (TL) has been shown to effectively reduce the calibration time in MI-BCIs. However, variations in data distribution among subjects can significantly influence the performance of TL in MI-BCIs.Approach.We propose a cross-dataset adaptive domain selection transfer learning framework that integrates domain selection, data alignment, and an enhanced common spatial pattern (CSP) algorithm. Our approach uses a huge dataset of 109 subjects as the source domain. We begin by identifying non-BCI illiterate subjects from this huge dataset, then determine the source domain subjects most closely aligned with the target subjects using maximum mean discrepancy. After undergoing Euclidean alignment processing, features are extracted by multiple composite CSP. The final classification is carried out using the support vector machine.Main results.Our findings indicate that the proposed technique outperforms existing methods, achieving classification accuracies of 75.05% and 76.82% in two cross-dataset experiments, respectively.Significance.By reducing the need for extensive training data, yet maintaining high accuracy, our method optimizes the practical implementation of MI-BCIs.