Could someone clarify to me why the state of art here at the forum involves converting EEGs into Spectrograms? What kind of information do we gain if we already have a 10 minutes long spectrogram for each sample?
", "messages": [ { "id": "2667075", "postDate": "02/24/2024 20:39:47", "content": "Could someone clarify to me why the state of art here at the forum involves converting EEGs into Spectrograms? What kind of information do we gain if we already have a 10 minutes long spectrogram for each sample?
", "rawMarkdown": "Could someone clarify to me why the state of art here at the forum involves converting EEGs into Spectrograms? What kind of information do we gain if we already have a 10 minutes long spectrogram for each sample?", "votes": null }, { "id": "2667111", "postDate": "02/24/2024 21:25:21", "content": "You can use CNNs
", "rawMarkdown": "You can use CNNs", "votes": null }, { "id": "2667137", "postDate": "02/24/2024 22:35:24", "content": "This channel will help you understand Signal theory.
", "rawMarkdown": "This [channel](https://www.youtube.com/watch?v=iCwMQJnKk2c&list=PL-wATfeyAMNqIee7cH3q1bh4QJFAaeNv0&ab_channel=ValerioVelardo-TheSoundofAI) will help you understand Signal theory.", "votes": null }, { "id": "2667269", "postDate": "02/25/2024 03:15:17", "content": "Converting electroencephalograms (EEGs) into spectrograms is a commonly used analytical method in neuroscience, cognitive science, and related fields, which contributes to a better understanding of the frequency characteristics of brain activity. This conversion is usually achieved by a Fourier transform (e.g., Fast Fourier Transform FFT), which can reveal the intensity of different frequency components of the brain's electrical activity. The following are the main reasons and benefits of converting EEG to spectrograms:
\nRevealing hidden information: EEG signals are time-series data, and it may be difficult to identify specific patterns or states of brain activity by observing them directly. Spectrograms enable us to identify specific frequency patterns of the brain in different states (e.g. awake, asleep, focused, etc.) by showing the energy distribution of different frequency components.
Simplified data analysis: Converting EEG signals into spectrograms allows for easier data analysis and interpretation. For example, specific cognitive tasks or emotional states may increase activity in certain frequency bands (e.g. alpha waves, beta waves, etc.). By analysing these changes, researchers can better understand how the brain responds to different stimuli or tasks.
Improved signal-to-noise ratio: In the frequency domain, signals from noisy or non-brain sources (e.g. EMG interference) can be more easily identified and filtered out, thus improving the quality of the signal. This is critical for improving the accuracy of EEG data analysis.
Easy feature extraction: In machine learning and data mining applications, it is much easier to extract features (e.g., power spectral density in a specific frequency band) from spectrograms than directly from raw time series. These features can be used to train models for classification, prediction, or other analytical tasks such as identifying neurological diseases, monitoring sleep quality, or assessing cognitive status.
Facilitating interdisciplinary research: Converting EEG to spectrograms and using these data for analysis can facilitate communication and collaboration between multiple fields, such as neuroscience, psychology, and computer science. This approach provides researchers with a common language to describe and compare brain activity patterns.
Supports real-time monitoring and feedback: In some applications, such as neurofeedback training, real-time conversion of EEG to spectrograms can help participants instantly see changes in their brain activity and adjust their mental state based on the feedback. This approach has been shown to be beneficial in improving attention deficit hyperactivity disorder (ADHD), anxiety, and other conditions.
In conclusion, converting EEG to spectrograms is a powerful and flexible tool that extends our understanding of changes in brain function and state and plays an important role in a variety of fields, including clinical diagnosis, cognitive research, and human-computer interaction.
", "rawMarkdown": "Converting electroencephalograms (EEGs) into spectrograms is a commonly used analytical method in neuroscience, cognitive science, and related fields, which contributes to a better understanding of the frequency characteristics of brain activity. This conversion is usually achieved by a Fourier transform (e.g., Fast Fourier Transform FFT), which can reveal the intensity of different frequency components of the brain's electrical activity. The following are the main reasons and benefits of converting EEG to spectrograms:\n\n1. **Revealing hidden information**: EEG signals are time-series data, and it may be difficult to identify specific patterns or states of brain activity by observing them directly. Spectrograms enable us to identify specific frequency patterns of the brain in different states (e.g. awake, asleep, focused, etc.) by showing the energy distribution of different frequency components.\n\n2. **Simplified data analysis**: Converting EEG signals into spectrograms allows for easier data analysis and interpretation. For example, specific cognitive tasks or emotional states may increase activity in certain frequency bands (e.g. alpha waves, beta waves, etc.). By analysing these changes, researchers can better understand how the brain responds to different stimuli or tasks.\n\n3. **Improved signal-to-noise ratio**: In the frequency domain, signals from noisy or non-brain sources (e.g. EMG interference) can be more easily identified and filtered out, thus improving the quality of the signal. This is critical for improving the accuracy of EEG data analysis.\n\n4. **Easy feature extraction**: In machine learning and data mining applications, it is much easier to extract features (e.g., power spectral density in a specific frequency band) from spectrograms than directly from raw time series. These features can be used to train models for classification, prediction, or other analytical tasks such as identifying neurological diseases, monitoring sleep quality, or assessing cognitive status.\n\n5. **Facilitating interdisciplinary research**: Converting EEG to spectrograms and using these data for analysis can facilitate communication and collaboration between multiple fields, such as neuroscience, psychology, and computer science. This approach provides researchers with a common language to describe and compare brain activity patterns.\n\n6. **Supports real-time monitoring and feedback**: In some applications, such as neurofeedback training, real-time conversion of EEG to spectrograms can help participants instantly see changes in their brain activity and adjust their mental state based on the feedback. This approach has been shown to be beneficial in improving attention deficit hyperactivity disorder (ADHD), anxiety, and other conditions.\n\nIn conclusion, converting EEG to spectrograms is a powerful and flexible tool that extends our understanding of changes in brain function and state and plays an important role in a variety of fields, including clinical diagnosis, cognitive research, and human-computer interaction.", "votes": null }, { "id": "2668070", "postDate": "02/25/2024 13:49:22", "content": "Thank you very much for your answer! It clarified a lot. Yet I don't understand why we should train the model with the eeg spectrograms at all, since we already have the 10 minutes long spectrograms. Isn't all the information already in the spectrograms provided by Kaggle?
", "rawMarkdown": "Thank you very much for your answer! It clarified a lot. Yet I don't understand why we should train the model with the eeg spectrograms at all, since we already have the 10 minutes long spectrograms. Isn't all the information already in the spectrograms provided by Kaggle?", "votes": null }, { "id": "2668907", "postDate": "02/26/2024 01:52:52", "content": "When working with EEG (electroencephalogram) data, particularly in the context of machine learning and data analysis, it's important to understand the nuances of the data and how different approaches to training models can impact the outcomes. The question you're asking touches on a fundamental aspect of signal processing and machine learning: why would one need to train a model on a spectrum of EEG data when a comprehensive spectrum covering a significant duration (like 10 minutes) is already available? Let's break this down.
\nEEG data captures the electrical activity of the brain through multiple electrodes placed on the scalp. This activity is highly dynamic, reflecting the complex interplay of neural circuits involved in different cognitive and physiological processes. The spectrum of EEG data refers to the frequency domain representation of these signals, which is crucial for identifying patterns related to specific brain states or responses.
\nVariability Over Time: Brain activity is not static; it varies significantly over short periods. A spectrum derived from a 10-minute segment provides a broad overview but may miss transient dynamics that are crucial for certain analyses or applications, such as detecting epileptic seizures or understanding attentional shifts.
Contextual Relevance: Depending on the application (e.g., brain-computer interfaces, sleep stage classification, cognitive load assessment), the relevance of different parts of the spectrum may change over time. Training models on a more granular temporal scale allows for capturing these nuances.
Non-Stationarity: EEG signals are non-stationary, meaning their statistical properties change over time. A model trained on a wide temporal spectrum might not perform well in recognizing patterns that occur in shorter, specific intervals.
Improved Model Generalization: By training on a variety of spectral features extracted from shorter segments, models can learn to generalize better across different states or conditions, enhancing their predictive accuracy and robustness.
Feature Resolution: Training on spectra from shorter segments can provide higher resolution in both frequency and time, allowing the model to detect subtle changes and patterns that might be averaged out or obscured in longer-duration spectra.
Customization for Specific Tasks: Certain tasks may benefit from focusing on specific time windows where relevant brain activity is more pronounced. Training models on these targeted segments can improve performance for specialized applications.
While a 10-minute spectrum provides a comprehensive overview, training models on spectra derived from shorter segments or varying durations can offer significant advantages in terms of temporal resolution, adaptability to non-stationary signals, and task-specific performance. This approach leverages the rich temporal dynamics of EEG data, enabling more nuanced analyses and potentially leading to better outcomes in applications ranging from clinical diagnostics to neurotechnology interfaces.
", "rawMarkdown": "When working with EEG (electroencephalogram) data, particularly in the context of machine learning and data analysis, it's important to understand the nuances of the data and how different approaches to training models can impact the outcomes. The question you're asking touches on a fundamental aspect of signal processing and machine learning: why would one need to train a model on a spectrum of EEG data when a comprehensive spectrum covering a significant duration (like 10 minutes) is already available? Let's break this down.\n\n### Understanding EEG Data\n\nEEG data captures the electrical activity of the brain through multiple electrodes placed on the scalp. This activity is highly dynamic, reflecting the complex interplay of neural circuits involved in different cognitive and physiological processes. The spectrum of EEG data refers to the frequency domain representation of these signals, which is crucial for identifying patterns related to specific brain states or responses.\n\n### The Role of Temporal Dynamics\n\n1. **Variability Over Time**: Brain activity is not static; it varies significantly over short periods. A spectrum derived from a 10-minute segment provides a broad overview but may miss transient dynamics that are crucial for certain analyses or applications, such as detecting epileptic seizures or understanding attentional shifts.\n\n2. **Contextual Relevance**: Depending on the application (e.g., brain-computer interfaces, sleep stage classification, cognitive load assessment), the relevance of different parts of the spectrum may change over time. Training models on a more granular temporal scale allows for capturing these nuances.\n\n3. **Non-Stationarity**: EEG signals are non-stationary, meaning their statistical properties change over time. A model trained on a wide temporal spectrum might not perform well in recognizing patterns that occur in shorter, specific intervals.\n\n### Advantages of Training on a Spectrum of EEG Data\n\n1. **Improved Model Generalization**: By training on a variety of spectral features extracted from shorter segments, models can learn to generalize better across different states or conditions, enhancing their predictive accuracy and robustness.\n\n2. **Feature Resolution**: Training on spectra from shorter segments can provide higher resolution in both frequency and time, allowing the model to detect subtle changes and patterns that might be averaged out or obscured in longer-duration spectra.\n\n3. **Customization for Specific Tasks**: Certain tasks may benefit from focusing on specific time windows where relevant brain activity is more pronounced. Training models on these targeted segments can improve performance for specialized applications.\n\n### Conclusion\n\nWhile a 10-minute spectrum provides a comprehensive overview, training models on spectra derived from shorter segments or varying durations can offer significant advantages in terms of temporal resolution, adaptability to non-stationary signals, and task-specific performance. This approach leverages the rich temporal dynamics of EEG data, enabling more nuanced analyses and potentially leading to better outcomes in applications ranging from clinical diagnostics to neurotechnology interfaces.", "votes": null }, { "id": "2669605", "postDate": "02/26/2024 11:20:50", "content": "Very short answer:
\nThe raw signal is in time domain (axis y = signal, axis x = time )
\nThe spectrogram is in frequency domain. (axis y = frequency, axis x = time, axis z = intensity)
The literarture divides the eeg signal in frequency bands:
\n\n\n\nThe observed frequencies are subdivided into various groups: alpha (8–13 Hz), beta (13–30 Hz), delta (0.5–4 Hz), and theta (4–7 Hz)
\n
Therefore, it makes sense to create a model that takes frequency information into account.
", "rawMarkdown": "Very short answer:\n\nThe raw signal is in time domain (axis y = signal, axis x = time )\nThe spectrogram is in frequency domain. (axis y = frequency, axis x = time, axis z = intensity)\n\nThe literarture divides the eeg signal in frequency bands: \n>The observed frequencies are subdivided into various groups: alpha (8–13 Hz), beta (13–30 Hz), delta (0.5–4 Hz), and theta (4–7 Hz)\n\n[source](https://en.wikipedia.org/wiki/Electroencephalography)\n\nTherefore, it makes sense to create a model that takes frequency information into account.", "votes": null }, { "id": "2671049", "postDate": "02/27/2024 09:00:53", "content": "Thank you very much for your answer!
", "rawMarkdown": "Thank you very much for your answer!", "votes": null }, { "id": "2698846", "postDate": "03/15/2024 16:56:46", "content": "I guess your question is really: why are we creating spectrograms from raw signals, when they give us spectrograms.
\nThe real issue with their spectrograms is we know nothing about how they're generated. They were the spectrograms shown to the labeller, but we don't know the parameters of the FFT done to generate them. I think what people usually hope for, in generating their own spectrograms from the raw signal data, is that they can control the FFT parameters, add signal filters (band-passes), add smoothing, etc. Perhaps this would make the spectrogram easier for the model to understand, and cut out some of the noise.
\nSecond advantage is that proper augmentations can be done with signal data more easily than with an image spectrogram. But you'll want to get the now-augmented signal data back into a spectrogram, somehow.
", "rawMarkdown": "I guess your question is really: why are we creating spectrograms from raw signals, when they give us spectrograms.\n\nThe real issue with their spectrograms is we know nothing about how they're generated. They were the spectrograms shown to the labeller, but we don't know the parameters of the FFT done to generate them. I think what people usually hope for, in generating their own spectrograms from the raw signal data, is that they can control the FFT parameters, add signal filters (band-passes), add smoothing, etc. Perhaps this would make the spectrogram easier for the model to understand, and cut out some of the noise.\n\nSecond advantage is that proper augmentations can be done with signal data more easily than with an image spectrogram. But you'll want to get the now-augmented signal data back into a spectrogram, somehow.", "votes": null } ], "comments": [ { "id": 2667111, "author_name": "alejopaullier", "author_url": "", "post_date": "02/24/2024 21:25:21", "content": "You can use CNNs
", "votes": null, "replies": [] }, { "id": 2667137, "author_name": "medali1992", "author_url": "", "post_date": "02/24/2024 22:35:24", "content": "This channel will help you understand Signal theory.
", "votes": null, "replies": [] }, { "id": 2667269, "author_name": "marshjff", "author_url": "", "post_date": "02/25/2024 03:15:17", "content": "Converting electroencephalograms (EEGs) into spectrograms is a commonly used analytical method in neuroscience, cognitive science, and related fields, which contributes to a better understanding of the frequency characteristics of brain activity. This conversion is usually achieved by a Fourier transform (e.g., Fast Fourier Transform FFT), which can reveal the intensity of different frequency components of the brain's electrical activity. The following are the main reasons and benefits of converting EEG to spectrograms:
\nRevealing hidden information: EEG signals are time-series data, and it may be difficult to identify specific patterns or states of brain activity by observing them directly. Spectrograms enable us to identify specific frequency patterns of the brain in different states (e.g. awake, asleep, focused, etc.) by showing the energy distribution of different frequency components.
Simplified data analysis: Converting EEG signals into spectrograms allows for easier data analysis and interpretation. For example, specific cognitive tasks or emotional states may increase activity in certain frequency bands (e.g. alpha waves, beta waves, etc.). By analysing these changes, researchers can better understand how the brain responds to different stimuli or tasks.
Improved signal-to-noise ratio: In the frequency domain, signals from noisy or non-brain sources (e.g. EMG interference) can be more easily identified and filtered out, thus improving the quality of the signal. This is critical for improving the accuracy of EEG data analysis.
Easy feature extraction: In machine learning and data mining applications, it is much easier to extract features (e.g., power spectral density in a specific frequency band) from spectrograms than directly from raw time series. These features can be used to train models for classification, prediction, or other analytical tasks such as identifying neurological diseases, monitoring sleep quality, or assessing cognitive status.
Facilitating interdisciplinary research: Converting EEG to spectrograms and using these data for analysis can facilitate communication and collaboration between multiple fields, such as neuroscience, psychology, and computer science. This approach provides researchers with a common language to describe and compare brain activity patterns.
Supports real-time monitoring and feedback: In some applications, such as neurofeedback training, real-time conversion of EEG to spectrograms can help participants instantly see changes in their brain activity and adjust their mental state based on the feedback. This approach has been shown to be beneficial in improving attention deficit hyperactivity disorder (ADHD), anxiety, and other conditions.
In conclusion, converting EEG to spectrograms is a powerful and flexible tool that extends our understanding of changes in brain function and state and plays an important role in a variety of fields, including clinical diagnosis, cognitive research, and human-computer interaction.
", "votes": null, "replies": [ { "id": 2668070, "author_name": "r3maia", "author_url": "", "post_date": "02/25/2024 13:49:22", "content": "Thank you very much for your answer! It clarified a lot. Yet I don't understand why we should train the model with the eeg spectrograms at all, since we already have the 10 minutes long spectrograms. Isn't all the information already in the spectrograms provided by Kaggle?
", "votes": null, "replies": [ { "id": 2668907, "author_name": "marshjff", "author_url": "", "post_date": "02/26/2024 01:52:52", "content": "When working with EEG (electroencephalogram) data, particularly in the context of machine learning and data analysis, it's important to understand the nuances of the data and how different approaches to training models can impact the outcomes. The question you're asking touches on a fundamental aspect of signal processing and machine learning: why would one need to train a model on a spectrum of EEG data when a comprehensive spectrum covering a significant duration (like 10 minutes) is already available? Let's break this down.
\nEEG data captures the electrical activity of the brain through multiple electrodes placed on the scalp. This activity is highly dynamic, reflecting the complex interplay of neural circuits involved in different cognitive and physiological processes. The spectrum of EEG data refers to the frequency domain representation of these signals, which is crucial for identifying patterns related to specific brain states or responses.
\nVariability Over Time: Brain activity is not static; it varies significantly over short periods. A spectrum derived from a 10-minute segment provides a broad overview but may miss transient dynamics that are crucial for certain analyses or applications, such as detecting epileptic seizures or understanding attentional shifts.
Contextual Relevance: Depending on the application (e.g., brain-computer interfaces, sleep stage classification, cognitive load assessment), the relevance of different parts of the spectrum may change over time. Training models on a more granular temporal scale allows for capturing these nuances.
Non-Stationarity: EEG signals are non-stationary, meaning their statistical properties change over time. A model trained on a wide temporal spectrum might not perform well in recognizing patterns that occur in shorter, specific intervals.
Improved Model Generalization: By training on a variety of spectral features extracted from shorter segments, models can learn to generalize better across different states or conditions, enhancing their predictive accuracy and robustness.
Feature Resolution: Training on spectra from shorter segments can provide higher resolution in both frequency and time, allowing the model to detect subtle changes and patterns that might be averaged out or obscured in longer-duration spectra.
Customization for Specific Tasks: Certain tasks may benefit from focusing on specific time windows where relevant brain activity is more pronounced. Training models on these targeted segments can improve performance for specialized applications.
While a 10-minute spectrum provides a comprehensive overview, training models on spectra derived from shorter segments or varying durations can offer significant advantages in terms of temporal resolution, adaptability to non-stationary signals, and task-specific performance. This approach leverages the rich temporal dynamics of EEG data, enabling more nuanced analyses and potentially leading to better outcomes in applications ranging from clinical diagnostics to neurotechnology interfaces.
", "votes": null, "replies": [ { "id": 2671049, "author_name": "r3maia", "author_url": "", "post_date": "02/27/2024 09:00:53", "content": "Thank you very much for your answer!
", "votes": null, "replies": [] } ] } ] } ] }, { "id": 2669605, "author_name": "serjhenrique", "author_url": "", "post_date": "02/26/2024 11:20:50", "content": "Very short answer:
\nThe raw signal is in time domain (axis y = signal, axis x = time )
\nThe spectrogram is in frequency domain. (axis y = frequency, axis x = time, axis z = intensity)
The literarture divides the eeg signal in frequency bands:
\n\n\n\nThe observed frequencies are subdivided into various groups: alpha (8–13 Hz), beta (13–30 Hz), delta (0.5–4 Hz), and theta (4–7 Hz)
\n
Therefore, it makes sense to create a model that takes frequency information into account.
", "votes": null, "replies": [] }, { "id": 2698846, "author_name": "mandeepc", "author_url": "", "post_date": "03/15/2024 16:56:46", "content": "I guess your question is really: why are we creating spectrograms from raw signals, when they give us spectrograms.
\nThe real issue with their spectrograms is we know nothing about how they're generated. They were the spectrograms shown to the labeller, but we don't know the parameters of the FFT done to generate them. I think what people usually hope for, in generating their own spectrograms from the raw signal data, is that they can control the FFT parameters, add signal filters (band-passes), add smoothing, etc. Perhaps this would make the spectrogram easier for the model to understand, and cut out some of the noise.
\nSecond advantage is that proper augmentations can be done with signal data more easily than with an image spectrogram. But you'll want to get the now-augmented signal data back into a spectrogram, somehow.
", "votes": null, "replies": [] } ], "raw_markdown_by_id": { "2667075": "Could someone clarify to me why the state of art here at the forum involves converting EEGs into Spectrograms? What kind of information do we gain if we already have a 10 minutes long spectrogram for each sample?", "2667111": "You can use CNNs", "2667137": "This [channel](https://www.youtube.com/watch?v=iCwMQJnKk2c&list=PL-wATfeyAMNqIee7cH3q1bh4QJFAaeNv0&ab_channel=ValerioVelardo-TheSoundofAI) will help you understand Signal theory.", "2667269": "Converting electroencephalograms (EEGs) into spectrograms is a commonly used analytical method in neuroscience, cognitive science, and related fields, which contributes to a better understanding of the frequency characteristics of brain activity. This conversion is usually achieved by a Fourier transform (e.g., Fast Fourier Transform FFT), which can reveal the intensity of different frequency components of the brain's electrical activity. The following are the main reasons and benefits of converting EEG to spectrograms:\n\n1. **Revealing hidden information**: EEG signals are time-series data, and it may be difficult to identify specific patterns or states of brain activity by observing them directly. Spectrograms enable us to identify specific frequency patterns of the brain in different states (e.g. awake, asleep, focused, etc.) by showing the energy distribution of different frequency components.\n\n2. **Simplified data analysis**: Converting EEG signals into spectrograms allows for easier data analysis and interpretation. For example, specific cognitive tasks or emotional states may increase activity in certain frequency bands (e.g. alpha waves, beta waves, etc.). By analysing these changes, researchers can better understand how the brain responds to different stimuli or tasks.\n\n3. **Improved signal-to-noise ratio**: In the frequency domain, signals from noisy or non-brain sources (e.g. EMG interference) can be more easily identified and filtered out, thus improving the quality of the signal. This is critical for improving the accuracy of EEG data analysis.\n\n4. **Easy feature extraction**: In machine learning and data mining applications, it is much easier to extract features (e.g., power spectral density in a specific frequency band) from spectrograms than directly from raw time series. These features can be used to train models for classification, prediction, or other analytical tasks such as identifying neurological diseases, monitoring sleep quality, or assessing cognitive status.\n\n5. **Facilitating interdisciplinary research**: Converting EEG to spectrograms and using these data for analysis can facilitate communication and collaboration between multiple fields, such as neuroscience, psychology, and computer science. This approach provides researchers with a common language to describe and compare brain activity patterns.\n\n6. **Supports real-time monitoring and feedback**: In some applications, such as neurofeedback training, real-time conversion of EEG to spectrograms can help participants instantly see changes in their brain activity and adjust their mental state based on the feedback. This approach has been shown to be beneficial in improving attention deficit hyperactivity disorder (ADHD), anxiety, and other conditions.\n\nIn conclusion, converting EEG to spectrograms is a powerful and flexible tool that extends our understanding of changes in brain function and state and plays an important role in a variety of fields, including clinical diagnosis, cognitive research, and human-computer interaction.", "2668070": "Thank you very much for your answer! It clarified a lot. Yet I don't understand why we should train the model with the eeg spectrograms at all, since we already have the 10 minutes long spectrograms. Isn't all the information already in the spectrograms provided by Kaggle?", "2668907": "When working with EEG (electroencephalogram) data, particularly in the context of machine learning and data analysis, it's important to understand the nuances of the data and how different approaches to training models can impact the outcomes. The question you're asking touches on a fundamental aspect of signal processing and machine learning: why would one need to train a model on a spectrum of EEG data when a comprehensive spectrum covering a significant duration (like 10 minutes) is already available? Let's break this down.\n\n### Understanding EEG Data\n\nEEG data captures the electrical activity of the brain through multiple electrodes placed on the scalp. This activity is highly dynamic, reflecting the complex interplay of neural circuits involved in different cognitive and physiological processes. The spectrum of EEG data refers to the frequency domain representation of these signals, which is crucial for identifying patterns related to specific brain states or responses.\n\n### The Role of Temporal Dynamics\n\n1. **Variability Over Time**: Brain activity is not static; it varies significantly over short periods. A spectrum derived from a 10-minute segment provides a broad overview but may miss transient dynamics that are crucial for certain analyses or applications, such as detecting epileptic seizures or understanding attentional shifts.\n\n2. **Contextual Relevance**: Depending on the application (e.g., brain-computer interfaces, sleep stage classification, cognitive load assessment), the relevance of different parts of the spectrum may change over time. Training models on a more granular temporal scale allows for capturing these nuances.\n\n3. **Non-Stationarity**: EEG signals are non-stationary, meaning their statistical properties change over time. A model trained on a wide temporal spectrum might not perform well in recognizing patterns that occur in shorter, specific intervals.\n\n### Advantages of Training on a Spectrum of EEG Data\n\n1. **Improved Model Generalization**: By training on a variety of spectral features extracted from shorter segments, models can learn to generalize better across different states or conditions, enhancing their predictive accuracy and robustness.\n\n2. **Feature Resolution**: Training on spectra from shorter segments can provide higher resolution in both frequency and time, allowing the model to detect subtle changes and patterns that might be averaged out or obscured in longer-duration spectra.\n\n3. **Customization for Specific Tasks**: Certain tasks may benefit from focusing on specific time windows where relevant brain activity is more pronounced. Training models on these targeted segments can improve performance for specialized applications.\n\n### Conclusion\n\nWhile a 10-minute spectrum provides a comprehensive overview, training models on spectra derived from shorter segments or varying durations can offer significant advantages in terms of temporal resolution, adaptability to non-stationary signals, and task-specific performance. This approach leverages the rich temporal dynamics of EEG data, enabling more nuanced analyses and potentially leading to better outcomes in applications ranging from clinical diagnostics to neurotechnology interfaces.", "2669605": "Very short answer:\n\nThe raw signal is in time domain (axis y = signal, axis x = time )\nThe spectrogram is in frequency domain. (axis y = frequency, axis x = time, axis z = intensity)\n\nThe literarture divides the eeg signal in frequency bands: \n>The observed frequencies are subdivided into various groups: alpha (8–13 Hz), beta (13–30 Hz), delta (0.5–4 Hz), and theta (4–7 Hz)\n\n[source](https://en.wikipedia.org/wiki/Electroencephalography)\n\nTherefore, it makes sense to create a model that takes frequency information into account.", "2671049": "Thank you very much for your answer!", "2698846": "I guess your question is really: why are we creating spectrograms from raw signals, when they give us spectrograms.\n\nThe real issue with their spectrograms is we know nothing about how they're generated. They were the spectrograms shown to the labeller, but we don't know the parameters of the FFT done to generate them. I think what people usually hope for, in generating their own spectrograms from the raw signal data, is that they can control the FFT parameters, add signal filters (band-passes), add smoothing, etc. Perhaps this would make the spectrogram easier for the model to understand, and cut out some of the noise.\n\nSecond advantage is that proper augmentations can be done with signal data more easily than with an image spectrogram. But you'll want to get the now-augmented signal data back into a spectrogram, somehow." }, "source": "meta" }