| --- |
| license: other |
| tags: |
| - eeg |
| - motor |
| - in-ear-eeg |
| - scalp-eeg |
| - hdf5 |
| --- |
| |
| # Motor-Processed |
|
|
| Processed HDF5 export of the **Wu et al. 2020** in-ear / scalp EEG motor dataset — paired |
| **in-ear EEG + high-density scalp EEG** recorded while 6 subjects performed a **2-class |
| left / right hand grasp** task. This repo holds the two processed HDF5 files (one per |
| modality); the raw Curry-7 recordings live on IEEE DataPort. |
|
|
| - **Paper:** Wu, Zhang, Fu, Cheung, Chan (2020). *An investigation of in-ear sensing for |
| motor task classification.* J. Neural Eng. 17 066010. |
| [10.1088/1741-2552/abc1b6](https://doi.org/10.1088/1741-2552/abc1b6) |
| - **Source (raw) dataset:** IEEE DataPort |
| [10.21227/j7rq-2p11](https://dx.doi.org/10.21227/j7rq-2p11). License: see the DataPort record. |
| - **Participants:** 6 adults (2 M / 4 F, ages 22–28), all right-handed. Approved by the City |
| University of Hong Kong ethics committee (ref. 2-25-201602_01). |
| |
| ## Files |
| |
| | File | Channels | Shape `(N, C, T)` | |
| |---|---|---:| |
| | `motor-in-ear-eeg.h5` | `LF, LB, LOU, LOD, RF, RB, ROU, ROD` (8 in-ear) | `(1114, 8, 3000)` | |
| | `motor-scalp-eeg.h5` | 122 scalp EEG (Quik-Cap C190, extended 10-05) | `(1114, 122, 3000)` | |
| |
| `T = 3000` is one 3-second epoch at 1000 Hz. `N = 1114` is the total number of trials — one |
| sample per trial. The two files are **strictly row-aligned**: sample `i` is the same trial |
| in both, so the in-ear and scalp views share label, `trial_id`, `split_group_id`, and window |
| bounds. For an ear-EEG foundation-model benchmark the 8 in-ear channels are the payload and |
| the 122 scalp channels are the gold-standard reference. |
|
|
| Label distribution (identical for both modalities): |
|
|
| | left | right | |
| |---:|---:| |
| | 557 | 557 | |
|
|
| ## Preprocessing |
|
|
| Generated with `Ear-EEG-FM-Benchmark/dataset/preprocess_motor.py` (schema/eegfm version |
| `0.5.0`), reading the raw Curry-7 files directly via MNE `read_raw_curry`: |
|
|
| - **one trial = one sample.** Each trial has a single arrow cue; it is extracted as one |
| **3-second epoch spanning [−1 s, +2 s] around arrow onset** — the classifier epoch used by |
| the paper. At the native 1000 Hz this is `T = 3000` samples. |
| - **0.1–100 Hz band-pass + 50 Hz notch**, applied to the full continuous recording *before* |
| slicing (avoids per-window filter edge effects). |
| - **no re-referencing, no resampling (kept at 1000 Hz), original channel names preserved** |
| (no near-ear renaming). Two modality groups only: 8 in-ear + 122 scalp. |
| - labels from the Curry event code: **1 → `left` (0)**, **2 → `right` (1)**; Curry system |
| markers (codes ≥ 1e5) are dropped. The `Trigger` and `REF` channels are used only to read |
| events and are never stored as data. |
| - **bad channels are recorded, not repaired:** subject-4's known-broken `FP1` is kept as-is |
| and listed in the `known_bad_channels_json` file attribute. Any real non-finite samples are |
| preserved (gaps interpolated only on a temporary filter copy, then the original NaN mask |
| restored) and reported in `nan_fraction` / `channel_nan_fraction`. |
|
|
| ## Task and labels |
|
|
| Each trial (10 s): a fixation cross (3 s; a warning beep 2 s after cross onset) → an **arrow |
| pointing left or right (pseudo-random) for 4 s**, during which the subject grasps (clenches) |
| the cued hand. The stored epoch is aligned to the **arrow onset** (= start of the grasp). |
|
|
| | Event code | Class | label | |
| |---:|---|---:| |
| | **1** | left hand | 0 | |
| | **2** | right hand | 1 | |
|
|
| The `1 = left, 2 = right` mapping is stated explicitly in the IEEE DataPort record. |
|
|
| ## Channels |
|
|
| The raw recording has 132 channels = **122 scalp EEG + 8 in-ear + Trigger + REF**; the |
| processed files keep only the 122 scalp and 8 in-ear signals (Trigger/REF dropped). The 8 |
| in-ear electrodes: |
|
|
| | Ear label | Position | |
| |---|---| |
| | `LF`, `LB` / `RF`, `RB` | left / right **ear-canal**, front & back | |
| | `LOU`, `LOD` / `ROU`, `ROD` | left / right **concha** (outer ear), upper & lower | |
|
|
| The scalp montage is the Neuroscan Quik-Cap C190 (extended 10-05): |
| `T9, FT9, FTT9h, T7, TP7, … C3, Cz, C4 … T8, TP8` (see `ch_names` in the file). All ear + |
| scalp channels were referenced to a shared scalp REF; scalp GRD was the ground. |
|
|
| ## Per-subject / per-run trial counts |
|
|
| Subjects 1, 5, 6 span several chronological runs (`run-01 …`, from the raw `motorN` |
| recordings); subjects 2, 3, 4 are single-run. Counts parsed from the source event files: |
|
|
| | Subject | Runs | Trials (L / R) | ≈ blocks (÷16) | |
| |---|---|---:|---:| |
| | sub-01 | 3 | 240 (120 / 120) | 15 | |
| | sub-02 | 1 | 160 (80 / 80) | 10 | |
| | sub-03 | 1 | 160 (80 / 80) | 10 | |
| | sub-04 | 1 | 80 (40 / 40) | 5 | |
| | sub-05 | 4 | 234 (117 / 117) | ~14.6 (irregular) | |
| | sub-06 | 3 | 240 (120 / 120) | 15 | |
| | **Total** | **13** | **1114 (557 / 557)** | — | |
|
|
| One block = 16 trials; perfectly class-balanced overall. |
|
|
| ## HDF5 schema (v0.5) |
|
|
| ```text |
| /data (N, C, 3000) float32 # µV at 1000 Hz |
| /durations (N,) int64 |
| /nan_fraction (N,) float32 |
| /channel_nan_fraction (N, C) float32 |
| /labels (N,) int64 # 0=left, 1=right |
| /sample_id (N,) int64 |
| /subject (N,) string # 'sub-01' … 'sub-06' |
| /session (N,) string # '' (no sessions) |
| /task (N,) string # 'grasp' |
| /acquisition (N,) string |
| /run (N,) string # 'run-01' … |
| /recording_id (N,) string |
| /trial_id (N,) int64 # arrow-event index within the run |
| /event_id (N,) int64 # Curry trigger code (1=left, 2=right) |
| /split_group_id (N,) int64 # unique per trial (trial-level splitting) |
| /window_start_sample (N,) int64 # epoch bounds in the continuous recording |
| /window_stop_sample (N,) int64 |
| /ch_names (C,) string |
| ``` |
|
|
| Key attributes: `sfreq` (1000.0), `class_names` (`['left','right']`), `unit` (`uV`), |
| `eegfm_version` (`0.5.0`), `preprocess_config_json`, `split_group_kind` (`trial`), |
| `event_id_kind`, `window_reference`, and `known_bad_channels_json` (`{"sub-04":["FP1"]}`). |
|
|
| No block grouping is baked in (`split_group_id` is unique per trial); block structure is |
| recoverable post-hoc from `window_start_sample` gaps (≈11 127-sample within-block cadence, |
| larger gaps between blocks). |
|
|
| ## Known issues / caveats |
|
|
| - **Task framing is described inconsistently across sources.** The paper says subjects "make |
| a fist" (motor **execution**); the IEEE DataPort record says "**imagine and grasp**". The |
| task is stored under the neutral name `grasp` — do not hard-assert MI vs ME. |
| - **subject-4** `FP1` is a known-broken channel (kept, not repaired — see Preprocessing). |
| - **subject-5** has 234 trials — not a clean multiple of the 16-trial block (split 80/60/30/64 |
| across 4 runs). |
| - **Block/subject counts in the paper / DataPort prose are unreliable** (DataPort is marked |
| "under construction" and disagrees with both the paper and the files). The counts here are |
| parsed directly from the source event files. |
|
|
| ## Provenance |
|
|
| Trial counts, channel lists, and the `1 = left / 2 = right` mapping were read from the raw |
| `.dap` / `.rs3` / `.ceo` files and cross-checked against the paper and the IEEE DataPort |
| record. Processed with `dataset/preprocess_motor.py` at eegfm schema `0.5.0`. |
|
|
